Paper Title Number 2
Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
K. K. Pal and K. S. Sudeep, 'Preprocessing for image classification by convolutional neural networks', 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2016, pp. 1778-1781. doi: 10.1109/RTEICT.2016.7808140
Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Prantik Howlader, Kuntal Kumar Pal, Alfredo Cuzzocrea, and S. D. Madhu Kumar. 2018. Predicting facebook-users' personality based on status and linguistic features via flexible regression analysis techniques. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC '18). ACM, New York, NY, USA, 339-345. DOI: https://doi.org/10.1145/3167132.3167166
Banerjee, P., Pal, K. K., Mitra, A., & Baral, C. (2019, July). Careful Selection of Knowledge to Solve Open Book Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 6120-6129).
@article{banerjee2019knowledge, title={Knowledge Guided Named Entity Recognition}, author={Banerjee, Pratyay and Pal, Kuntal Kumar and Devarakonda, Murthy and Baral, Chitta}, journal={arXiv preprint arXiv:1911.03869}, year={2019} }
@@article{baral2020natural, title={Natural language qa approaches using reasoning with external knowledge}, author={Baral, Chitta and Banerjee, Pratyay and Pal, Kuntal Kumar and Mitra, Arindam}, journal={arXiv preprint arXiv:2003.03446}, year={2020} }
@article{banerjee2020can, title={Can Transformers Reason About Effects of Actions?}, author={Banerjee, Pratyay and Baral, Chitta and Luo, Man and Mitra, Arindam and Pal, Kuntal and Son, Tran C and Varshney, Neeraj}, journal={arXiv preprint arXiv:2012.09938}, year={2020} }
@@article{banerjee2021variable, title={Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modeling}, author={Banerjee, Pratyay and Pal, Kuntal Kumar and Wang, Fish and Baral, Chitta}, journal={arXiv preprint arXiv:2103.12801}, year={2021} }
@article{10.1145/3465221, author = {Banerjee, Pratyay and Pal, Kuntal Kumar and Devarakonda, Murthy and Baral, Chitta}, title = {Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering}, year = {2021}, issue_date = {October 2021}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {4}, issn = {2691-1957}, url = {https://doi.org/10.1145/3465221}, doi = {10.1145/3465221}, abstract = {In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B, I, O only) compared to multiple entity-classes in traditional NER (i.e., Bentity1, Bentity2, Ientity1, I, O), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.}, journal = {ACM Trans. Comput. Healthcare}, month = jul, articleno = {33}, numpages = {24}, keywords = {BIO tagging, multitask training, Named entity recognition, transfer learning, biomedical, NER, question answering, text tagging, BERT-CNN} }
@article{pal-etal-2021-constructing, title = "Constructing Flow Graphs from Procedural Cybersecurity Texts", author = "Pal, Kuntal Kumar and Kashihara, Kazuaki and Banerjee, Pratyay and Mishra, Swaroop and Wang, Ruoyu and Baral, Chitta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.345", doi = "10.18653/v1/2021.findings-acl.345", pages = "3945--3957", }
@article{pal2021investigating, title={Investigating Numeracy Learning Ability of a Text-to-Text Transfer Model}, author={Pal, Kuntal Kumar and Baral, Chitta}, journal={arXiv preprint arXiv:2109.04672}, year={2021} }
@inproceedings{banerjee2021commonsense, title={Commonsense Reasoning with Implicit Knowledge in Natural Language}, author={Banerjee, Pratyay and Mishra, Swaroop and Pal, Kuntal Kumar and Mitra, Arindam and Baral, Chitta}, booktitle={3rd Conference on Automated Knowledge Base Construction}, year={2021} }
Talk at UC San Francisco, Department of Testing, San Francisco, California
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk at London School of Testing, London, UK
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA