Research Interests
NLP for Social Good | AI for Healthcare

Publications

• Gender Tagging of Named Entities using Retrieval-assisted Multi-context Aggregation: An Unsupervised Approach
  Sudeshna Das and Jiaul H Paik
  Journal of the Association for Information Science and Technology, Volume 74, Issue 4, 2023
  TL;DR | Paper | BibTeX | Dataset
  
  Existing approaches toward name gender identification rely exclusively on using the gender distributions from labeled data. In the absence of such labeled data, these methods fail. We propose a two-stage model that is able to infer the gender of names present in text without requiring explicit name-gender labels. We use coreference resolution as the backbone for our proposed model. To aid coreference resolution where the existing contextual information does not suffice, we use a retrieval-assisted context aggregation framework. We demonstrate that state-of-the-art name gender inference is possible without supervision.
  
  
  @article{das2023gendertag,
  title={Gender tagging of Named Entities using Retrieval-assisted Multi-context Aggregation: An Unsupervised Approach},
  author={Das, Sudeshna and Paik, Jiaul H},
  journal={Journal of the Association for Information Science and Technology},
  volume={74},
  number={4},
  pages={461-475},
  year={2023},
  publisher={Wiley}
  }
  
  
• Context-Sensitive Gender Inference of Named Entities in Text
  Sudeshna Das and Jiaul H Paik
  Information Processing & Management, Volume 58, Issue 1, 2021
  TL;DR | Paper | BibTeX | Dataset
  
  Gender tagging of named entities has traditionally been database-reliant and insensitive to context. The same gender is assigned to "Alex" in both "Alex is a good boy" and "Alex is a smart girl". We propose a novel context-sensitive supervised approach based on the transformer network to identify the gender of named entities. We also create four open-source datasets from well-known NER corpora and make them publicly available.
  
  
  @article{das2021context,
  title={Context-Sensitive Gender Inference of Named Entities in Text},
  author={Das, Sudeshna and Paik, Jiaul H},
  journal={Information Processing \& Management},
  volume={58},
  number={1},
  pages={102423},
  year={2021},
  publisher={Elsevier}
  }
  
  
• Resilience of Named Entity Recognition Models Under Adversarial Attack
  Sudeshna Das and Jiaul H Paik
  DADC Workshop, NAACL 2022
  TL;DR | Paper | BibTeX | Code | Slides
  
  In this paper we present four model-agnostic adversarial attacks to gauge the resilience of NER models for noisy input text. Our experiments with state-of-the-art NER methods on five English NER datasets suggest that the NER models are over-reliant on case information and do not utilise contextual information well.
  
  
  @inproceedings{das-paik-2022-resilience,
  title = {Resilience of Named Entity Recognition Models under Adversarial Attack},
  author = {Das, Sudeshna and Paik, Jiaul H},
  booktitle = {Proceedings of the First Workshop on Dynamic Adversarial Data Collection},
  month = {jul},
  year = {2022},
  address = {Seattle, WA},
  publisher = {Association for Computational Linguistics},
  url = {https://aclanthology.org/2022.dadc-1.1},
  pages = {1--6},
  }
  
  
• Better Textbooks with Human Language Technology
  Sudeshna Das
  Doctoral Consortium, SIGIR 2018
  TL;DR | Paper | BibTeX
  
  Textbooks are widely used as the primary teaching and learning material all over the world. Due to the pervasive nature of textbooks, it is essential to ensure that textbooks adhere to specific standards. Subject experts are required to comprehensively study the material at hand to verify compliance with any such standards set. This is a costly exercise. We propose a feasible alternative to reduce the costs associated with three tasks that improve textbook quality: (a) identification of gender bias in textbooks, (b) optimal ordering of concepts in a textbook, and (c) generating supplementary study material at the same difficulty level.
  
  
  @inproceedings{das2018better,
  title={Better Textbooks with Human Language Technology},
  author={Das, Sudeshna},
  booktitle={The 41st International ACM SIGIR Conference on Research \& Development in Information Retrieval},
  pages={1465--1465},
  year={2018}
  }
  
  

Selected Bibliography
DBLP | ACM Digital Library | Semantic Scholar | Google Scholar | ORCiD | OpenReview