Please visit the Google Scholar Page for full publications.

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }

1. WWW

   Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification

   Abdullah Mazhar, Zuhair Hasan Shaik, Aseem Srivastava, Polly Ruhnke, Lavanya Vaddavalli, Sri Keshav Katragadda, Shweta Yadav, and Md Shad Akhtar

   In Proceedings of the ACM on Web Conference 2025

   Bib

   @inproceedings{mazhar2025figurative,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification},
     author = {Mazhar, Abdullah and Shaik, Zuhair Hasan and Srivastava, Aseem and Ruhnke, Polly and Vaddavalli, Lavanya and Katragadda, Sri Keshav and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Proceedings of the ACM on Web Conference},
     pages = {637--648},
     year = {2025},
     selected = {true}
   }

2. ACL (Findings)

   No perspective, no perception!! Perspective-aware Healthcare Answer Summarization

   Gauri Naik, Sharad Chandakacherla, Shweta Yadav, and Md Shad Akhtar

   In Findings of the Association for Computational Linguistics ACL 2024

   Bib

   @inproceedings{naik2024no,
     abbr = {ACL (Findings)},
     bibtex_show = {true},
     title = {No perspective, no perception!! Perspective-aware Healthcare Answer Summarization},
     author = {Naik, Gauri and Chandakacherla, Sharad and Yadav, Shweta and Akhtar, Md Shad},
     booktitle = {Findings of the Association for Computational Linguistics ACL},
     pages = {15919--15932},
     year = {2024},
     selected = {true}
   }

3. ACL

   Towards Identifying Fine-Grained Depression Symptoms from Memes

   Shweta Yadav, Cornelia Caragea, Chenye Zhao, Naincy Kumari, Marvin Solberg, and Tanmay Sharma

   In the Proceedings of the Association for Computational Linguistics (ACL) 2023

   Bib

   @inproceedings{yadav2023acl,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Towards Identifying Fine-Grained Depression Symptoms from Memes},
     author = {Yadav, Shweta and Caragea, Cornelia and Zhao, Chenye and Kumari, Naincy and Solberg, Marvin and Sharma, Tanmay},
     booktitle = {the Proceedings of the Association for Computational Linguistics (ACL)},
     year = {2023},
     selected = {true}
   }

4. WWW

   Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning

   Shweta Yadav, Stefan Cobeli, and Cornelia Caragea

   In the Proceedings of the ACM Web Conference (WWW) 2023

   Bib

   @inproceedings{yadav2023theweb,
     abbr = {WWW},
     bibtex_show = {true},
     title = {Towards Understanding Consumer Healthcare Questions on the Web with Semantically Enhanced Contrastive Learning},
     author = {Yadav, Shweta and Cobeli, Stefan and Caragea, Cornelia},
     booktitle = {the Proceedings of the ACM Web Conference (WWW)},
     year = {2023},
     selected = {true}
   }

5. COLING

   Towards Summarizing Healthcare Questions in Low-Resource Setting

   Shweta Yadav, and Cornelia Caragea

   In the Proceedings of the 29th International Conference on Computational Linguistics (COLING) 2022

   Bib

   @inproceedings{yadav2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Summarizing Healthcare Questions in Low-Resource Setting},
     author = {Yadav, Shweta and Caragea, Cornelia},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {true}
   }

6. ACL

   Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   In ACL 2021

   Bib

   @inproceedings{yadav2021reinforced,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     booktitle = {ACL},
     year = {2021},
     selected = {true}
   }

7. ACL

   Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction

   Sriram Pingali, Shweta Yadav, Pratik Dutta, and Sriparna Saha

   In ACL Findings 2021

   Bib

   @inproceedings{Sriram2021multimodal,
     abbr = {ACL},
     bibtex_show = {true},
     title = {Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction},
     author = {Pingali, Sriram and Yadav, Shweta and Dutta, Pratik and Saha, Sriparna},
     booktitle = {ACL Findings},
     year = {2021},
     selected = {true}
   }

8. JBI

   Question-aware transformer models for consumer health question summarization

   Shweta Yadav, Deepak Gupta, Asma Ben Abacha, and Dina Demner-Fushman

   Journal of Biomedical Informatics 2022

   Bib

   @article{yadav2022question,
     abbr = {JBI},
     bibtex_show = {true},
     title = {Question-aware transformer models for consumer health question summarization},
     author = {Yadav, Shweta and Gupta, Deepak and Abacha, Asma Ben and Demner-Fushman, Dina},
     journal = {Journal of Biomedical Informatics},
     volume = {128},
     pages = {104040},
     year = {2022},
     publisher = {Elsevier},
     selected = {true}
   }

9. PLOS ONE

   “When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction

   Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, and Amit Sheth

   PLoS one 2021

   Bib

   @article{yadav2021they,
     abbr = {PLOS ONE},
     bibtex_show = {true},
     title = {“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware Attention Framework for Relationship Extraction},
     author = {Yadav, Shweta and Lokala, Usha and Daniulaityte, Raminta and Thirunarayan, Krishnaprasad and Lamy, Francois and Sheth, Amit},
     journal = {PLoS one},
     volume = {16},
     number = {3},
     pages = {e0248299},
     year = {2021},
     selected = {true},
     publisher = {Public Library of Science San Francisco, CA USA}
   }

10. ACL

    A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

    Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

    In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) 2019

    Bib

    @inproceedings{yadav2019unified,
      abbr = {ACL},
      bibtex_show = {true},
      title = {A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining},
      author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
      pages = {5234--5245},
      year = {2019},
      selected = {true}
    }

11. The Web (WWW)

    EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection

    Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, and Amit Sheth

    In Proceedings of The Web Conference 2020 (WWW 2020) 2020

    Abs Bib

    Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.

    @inproceedings{10.1145/3366423.3380263,
      abbr = {The Web (WWW)},
      bibtex_show = {true},
      author = {Kumar, Ramnath and Yadav, Shweta and Daniulaityte, Raminta and Lamy, Francois and Thirunarayan, Krishnaprasad and Lokala, Usha and Sheth, Amit},
      title = {EDarkFind: Unsupervised Multi-View Learning for Sybil Account Detection},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3366423.3380263},
      doi = {10.1145/3366423.3380263},
      booktitle = {Proceedings of The Web Conference 2020 (WWW 2020)},
      pages = {1955–1965},
      numpages = {11},
      keywords = {Sybil Detection, Darknet Market, Correlation Analysis, Stylometry, Drug Trafficker Identification, Multi-view Learning},
      location = {Taipei, Taiwan},
      series = {WWW '20},
      selected = {true}
    }

12. IEEE TCBB

    Relation extraction from biomedical and clinical text: Unified multitask learning framework

    Shweta Yadav, Srivastsa Ramesh, Sriparna Saha, and Asif Ekbal

    IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020

    Bib

    @article{yadav2020relation,
      title = {Relation extraction from biomedical and clinical text: Unified multitask learning framework},
      author = {Yadav, Shweta and Ramesh, Srivastsa and Saha, Sriparna and Ekbal, Asif},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2020},
      publisher = {IEEE},
      abbr = {IEEE TCBB},
      bibtex_show = {true},
      selected = {true}
    }