2023

1. 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}
   }

2. 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}
   }

2022

1. 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}
   }

2. COLING

   Towards Enhancing Health Coaching Dialogue in Low-Resource Settings

   Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, and Shweta Yadav

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

   Bib

   @inproceedings{yue2022coling,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Towards Enhancing Health Coaching Dialogue in Low-Resource Settings},
     author = {Zhou, Yue and Eugenio, Barbara Di and Ziebart, Brian and Sharp, Lisa and Liu, Bing and Gerber, Ben and Agadakos, Nikolaos and Yadav, Shweta},
     booktitle = {the Proceedings of the 29th International Conference on Computational Linguistics (COLING)},
     year = {2022},
     selected = {false}
   }

3. LREC

   Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism

   Stefan Cobeli, Ioan-Bogdan Iordache, Shweta Yadav, Cornelia Caragea, Liviu P. Dinu, and Dragos Iliescu

   In Proceedings of the 2022 International Conference on Language Resources and Evaluation (LREC) 2022

   Bib

   @inproceedings{stefan2022detecting,
     abbr = {LREC},
     bibtex_show = {true},
     title = {Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism},
     author = {Cobeli, Stefan and Iordache, Ioan-Bogdan and Yadav, Shweta and Caragea, Cornelia and Dinu, Liviu P. and Iliescu, Dragos},
     booktitle = {Proceedings of the 2022 International Conference on Language Resources and Evaluation (LREC)},
     pages = {2032--2041},
     year = {2022}
   }

4. SBP-BRiMS

   How is Vaping Framed on Online Knowledge Dissemination Platforms?

   Keyu Chen, Yiwen Shi, Jun Luo, Joyce Jiang, Shweta Yadav, Munmun De Choudhury, Ashique Khudabukhsh, Marzieh Babaeianjelodar, Frederick Altice, and Navin Kumar

   In To appear in the Proceedings of the 15th International Conference on Social Computing, Behavioral-Cultural Modeling Prediction and Behavior Representation in Modeling and Simulation. (SBP-BRiMS) 2022

   Bib

   @inproceedings{yadav2020identifying,
     abbr = {SBP-BRiMS},
     bibtex_show = {true},
     title = {How is Vaping Framed on Online Knowledge Dissemination Platforms?},
     author = {Chen, Keyu and Shi, Yiwen and Luo, Jun and Jiang, Joyce and Yadav, Shweta and Choudhury, Munmun De and Khudabukhsh, Ashique and Babaeianjelodar, Marzieh and Altice, Frederick and Kumar, Navin},
     booktitle = {To appear in the Proceedings of the 15th International Conference on Social
     Computing, Behavioral-Cultural Modeling Prediction and Behavior Representation in
     Modeling and Simulation. (SBP-BRiMS)},
     year = {2022}
   }

5. 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}
   }

2021

1. 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}
   }

2. 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}
   }

3. 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}
   }

4. BioNLP

   NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization

   Shweta Yadav, Mourad Sarrouti, and Deepak Gupta

   In Proceedings of the 20th Workshop on Biomedical Language Processing 2021

   Abs Bib PDF

   The quest for seeking health information has swamped the web with consumers’ healthrelated questions, which makes the need for efficient and reliable question answering systems more pressing. The consumers’ questions, however, are very descriptive and contain several peripheral information (like patient’s medical history, demographic information, etc.), that are often not required for answering the question. Furthermore, it contributes to the challenges of understanding natural language questions for automatic answer retrieval. Also, it is crucial to provide the consumers with the exact and relevant answers, rather than the entire pool of answer documents to their question. One of the cardinal tasks in achieving robust consumer health question answering systems is the question summarization and multi-document answer summarization. This paper describes the participation of the U.S. National Library of Medicine (NLM) in Consumer Question and Multi-Answer Summarization tasks of the MEDIQA 2021 challenge at NAACL-BioNLP workshop. In this work, we exploited the capabilities of pre-trained transformer models and introduced a transfer learning approach for the abstractive Question Summarization and extractive Multi-Answer Summarization tasks by first pre-training our model on a task-specific summarization dataset followed by fine-tuning it for both the tasks via incorporating medical entities. We achieved the second, sixth and the fourth position for the Question Summarization task in terms ROUGE-1, ROUGE-2 and ROUGE-L scores respectively.

   @inproceedings{yadav-etal-2021-nlm,
     title = {{NLM} at {MEDIQA} 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization},
     author = {Yadav, Shweta and Sarrouti, Mourad and Gupta, Deepak},
     booktitle = {Proceedings of the 20th Workshop on Biomedical Language Processing},
     month = jun,
     year = {2021},
     address = {Online},
     abbr = {BioNLP},
     bibtex_show = {true},
     pdf = {https://www.aclweb.org/anthology/2021.bionlp-1.34.pdf},
     publisher = {Association for Computational Linguistics},
     url = {https://www.aclweb.org/anthology/2021.bionlp-1.34},
     doi = {10.18653/v1/2021.bionlp-1.34},
     pages = {291--301}
   }

2020

1. COLING

   Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

   Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, and Jeremiah Schumm

   In Proceedings of the 28th International Conference on Computational Linguistics (COLING) 2020

   Bib

   @inproceedings{yadav2020identifyinh,
     abbr = {COLING},
     bibtex_show = {true},
     title = {Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework},
     author = {Yadav, Shweta and Chauhan, Jainish and Sain, Joy Prakash and Thirunarayan, Krishnaprasad and Sheth, Amit and Schumm, Jeremiah},
     booktitle = {Proceedings of the 28th International Conference on Computational Linguistics (COLING)},
     pages = {696--709},
     year = {2020}
   }

2. 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}
   }

3. COLING

   Medical Knowledge-enriched Textual Entailment Framework

   Shweta Yadav, Vishal Pallagani, and Amit Sheth

   In Proceedings of the 28th International Conference on Computational Linguistics 2020

   Abs Bib PDF

   One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the con-text beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. We evaluate our framework on the benchmark MEDIQA-RQE dataset and manifest that the use of knowledge-enriched dual-encoding mechanism help in achieving an absolute improvement of 8.27% over SOTA language models.

   @inproceedings{yadav-etal-2020-medical,
     title = {Medical Knowledge-enriched Textual Entailment Framework},
     author = {Yadav, Shweta and Pallagani, Vishal and Sheth, Amit},
     abbr = {COLING},
     bibtex_show = {true},
     booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
     month = dec,
     year = {2020},
     pdf = {https://www.aclweb.org/anthology/2020.coling-main.161.pdf},
     address = {Barcelona, Spain (Online)},
     publisher = {International Committee on Computational Linguistics},
     url = {https://www.aclweb.org/anthology/2020.coling-main.161},
     doi = {10.18653/v1/2020.coling-main.161},
     pages = {1795--1801}
   }

4. ACM Tutorial

   Knowledge-infused Deep Learning

   Manas Gaur, Ugur Kursuncu, Amit Sheth, Ruwan Wickramarachchi, and Shweta Yadav

   In Proceedings of the 31st ACM Conference on Hypertext and Social Media 2020

   Bib

   @inproceedings{gaur2020knowledge,
     title = {Knowledge-infused Deep Learning},
     author = {Gaur, Manas and Kursuncu, Ugur and Sheth, Amit and Wickramarachchi, Ruwan and Yadav, Shweta},
     booktitle = {Proceedings of the 31st ACM Conference on Hypertext and Social Media},
     pages = {309--310},
     abbr = {ACM Tutorial},
     bibtex_show = {true},
     year = {2020}
   }

5. Book Chapter

   Patient data de-identification: a conditional random-field-based supervised approach

   Shweta Yadav, Asif Ekbal, Sriparna Saha, Parth S Pathak, and Pushpak Bhattacharyya

   2020

   Bib

   @incollection{yadav2020patient,
     title = {Patient data de-identification: a conditional random-field-based supervised approach},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Pathak, Parth S and Bhattacharyya, Pushpak},
     booktitle = {Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications},
     pages = {1502--1521},
     year = {2020},
     abbr = {Book Chapter},
     bibtex_show = {true},
     publisher = {IGI Global}
   }

6. ACM TOMM

   Exploring Disorder-Aware Attention for Clinical Event Extraction

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

   ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 2020

   Bib

   @article{yadav2020exploring,
     title = {Exploring Disorder-Aware Attention for Clinical Event Extraction},
     author = {Yadav, Shweta and Ramteke, Pralay and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
     journal = {ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},
     volume = {16},
     number = {1s},
     pages = {1--21},
     abbr = {ACM TOMM},
     bibtex_show = {true},
     year = {2020},
     publisher = {ACM New York, NY, USA}
   }

7. 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}
   }

8. ICPR

   Assessing the severity of health states based on social media posts

   Shweta Yadav, Joy Prakash Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

   2020 25th International Conference on Pattern Recognition (ICPR 2020) 2020

   Bib

   @article{yadav2020assessing,
     abbr = {ICPR},
     bibtex_show = {true},
     title = {Assessing the severity of health states based on social media posts},
     author = {Yadav, Shweta and Sain, Joy Prakash and Sheth, Amit and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
     journal = {2020 25th International Conference on Pattern Recognition (ICPR 2020)},
     year = {2020}
   }

2019

1. 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}
   }

2. Springer CMOT

   Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio

   Usha Lokala, Francois R Lamy, Raminta Daniulaityte, Amit Sheth, Ramzi W Nahhas, Jason I Roden, Shweta Yadav, and Robert G Carlson

   Computational and mathematical organization theory 2019

   Bib

   @article{lokala2019global,
     title = {Global trends, local harms: availability of fentanyl-type drugs on the dark web and accidental overdoses in Ohio},
     author = {Lokala, Usha and Lamy, Francois R and Daniulaityte, Raminta and Sheth, Amit and Nahhas, Ramzi W and Roden, Jason I and Yadav, Shweta and Carlson, Robert G},
     journal = {Computational and mathematical organization theory},
     volume = {25},
     number = {1},
     pages = {48--59},
     year = {2019},
     abbr = {Springer CMOT},
     bibtex_show = {true},
     publisher = {Springer}
   }

3. KBS

   Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein–protein interaction

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

   Knowledge-Based Systems 2019

   Bib

   @article{yadav2019feature,
     abbr = {KBS},
     bibtex_show = {true},
     title = {Feature assisted stacked attentive shortest dependency path based Bi-LSTM model for protein--protein interaction},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Kumar, Ankit and Bhattacharyya, Pushpak},
     journal = {Knowledge-Based Systems},
     volume = {166},
     pages = {18--29},
     year = {2019},
     publisher = {Elsevier}
   }

2018

1. LREC

   Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System

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

   In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) 2018

   Bib

   @inproceedings{yadav-etal-2018-medical,
     title = {Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
     abbr = {LREC},
     bibtex_show = {true},
     booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)},
     month = may,
     year = {2018},
     address = {Miyazaki, Japan},
     publisher = {European Language Resources Association (ELRA)},
     url = {https://www.aclweb.org/anthology/L18-1442}
   }

2. NAACL

   Multi-task learning framework for mining crowd intelligence towards clinical treatment

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

   In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018): Human Language Technologies, Volume 2 (Short Papers) 2018

   Bib

   @inproceedings{yadav2018multi,
     abbr = {NAACL},
     bibtex_show = {true},
     title = {Multi-task learning framework for mining crowd intelligence towards clinical treatment},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak and Sheth, Amit},
     booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018): Human Language Technologies, Volume 2 (Short Papers)},
     pages = {271--277},
     year = {2018}
   }

3. Soft Computing

   Feature selection for entity extraction from multiple biomedical corpora: A PSO-based approach

   Shweta Yadav, Asif Ekbal, and Sriparna Saha

   Soft Computing 2018

   Bib

   @article{yadav2018feature,
     abbr = {Soft Computing},
     bibtex_show = {true},
     title = {Feature selection for entity extraction from multiple biomedical corpora: A PSO-based approach},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna},
     journal = {Soft Computing},
     volume = {22},
     number = {20},
     pages = {6881--6904},
     year = {2018},
     publisher = {Springer}
   }

2017

1. EACL

   Entity extraction in biomedical corpora: An approach to evaluate word embedding features with pso based feature selection

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

   In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017): Volume 1, Long Papers 2017

   Bib

   @inproceedings{yadav2017entity,
     abbr = {EACL},
     bibtex_show = {true},
     title = {Entity extraction in biomedical corpora: An approach to evaluate word embedding features with pso based feature selection},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
     booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017): Volume 1, Long Papers},
     pages = {1159--1170},
     year = {2017}
   }

2016

1. ClinicalNLP

   Deep learning architecture for patient data de-identification in clinical records

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

   In Proceedings of the clinical natural language processing workshop (ClinicalNLP) 2016

   Bib

   @inproceedings{yadav2016deep,
     abbr = {ClinicalNLP},
     bibtex_show = {true},
     title = {Deep learning architecture for patient data de-identification in clinical records},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
     booktitle = {Proceedings of the clinical natural language processing workshop (ClinicalNLP)},
     pages = {32--41},
     year = {2016}
   }

2. CEUR

   A hybrid approach for entity extraction in code-mixed social media data

   Deepak Gupta, Shubham Tripathi, Asif Ekbal, and Pushpak Bhattacharyya

   CEUR 2016

   Bib

   @article{gupta2016hybrid,
     title = {A hybrid approach for entity extraction in code-mixed social media data},
     author = {Gupta, Deepak and Tripathi, Shubham and Ekbal, Asif and Bhattacharyya, Pushpak},
     journal = {CEUR},
     abbr = {CEUR},
     bibtex_show = {true},
     volume = {25},
     pages = {66},
     year = {2016}
   }

3. ICON

   A Recurrent Neural Network Architecture for De-identifying Clinical Records

   Shweta, Ankit Kumar, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya

   In Proceedings of the 13th International Conference on Natural Language Processing 2016

   Bib

   @inproceedings{shweta-etal-2016-recurrent,
     title = {A Recurrent Neural Network Architecture for De-identifying Clinical Records},
     author = {{Shweta} and Kumar, Ankit and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak},
     booktitle = {Proceedings of the 13th International Conference on Natural Language Processing},
     month = dec,
     year = {2016},
     abbr = {ICON},
     bibtex_show = {true},
     address = {Varanasi, India},
     publisher = {NLP Association of India},
     url = {https://www.aclweb.org/anthology/W16-6325},
     pages = {188--197}
   }

4. ICPR

   A Deep Learning Architecture for Protein-protein Interaction Article Identification

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

   In 2016 23rd International Conference on Pattern Recognition (ICPR 2016) 2016

   Bib

   @inproceedings{ekbal2016deep,
     abbr = {ICPR},
     bibtex_show = {true},
     title = {A Deep Learning Architecture for Protein-protein Interaction Article Identification},
     author = {Yadav, Shweta and Ekbal, Asif and Saha, Sriparna and Bhattacharyya, Pushpak and others},
     booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR 2016)},
     pages = {3128--3133},
     year = {2016},
     organization = {IEEE}
   }

2015

1. NLDB

   Pso-asent: Feature selection using particle swarm optimization for aspect based sentiment analysis

   Deepak Kumar Gupta, Kandula Srikanth Reddy, Shweta Yadav, and Asif Ekbal

   In International conference on applications of natural language to information systems 2015

   Bib

   @inproceedings{gupta2015pso,
     title = {Pso-asent: Feature selection using particle swarm optimization for aspect based sentiment analysis},
     author = {Gupta, Deepak Kumar and Reddy, Kandula Srikanth and Yadav, Shweta and Ekbal, Asif},
     booktitle = {International conference on applications of natural language to information systems},
     pages = {220--233},
     year = {2015},
     abbr = {NLDB},
     bibtex_show = {true},
     organization = {Springer}
   }