Research Interests
NLP for Social Good | AI for Healthcare
Selected Publications
Few-shot Learning for Medical Text: A Review of Advances, Trends, and Opportunities Yao Ge, Yuting Guo, Sudeshna Das, Mohammed Ali Al-Garadi and Abeed Sarker Journal of Biomedical Informatics, Volume 144, 2023 TL;DR | Paper | BibTeX
Few-shot learning (FSL) is a class of machine learning methods that require small numbers of labeled instances for training. With many medical topics having limited annotated text-based data in practical settings, FSL-based natural language processing (NLP) holds substantial promise. We aimed to conduct a review to explore the current state of FSL methods for medical NLP.
@article{ge2023few,
title={Few-shot learning for medical text: A review of advances, trends, and opportunities},
author={Ge, Yao and Guo, Yuting and Das, Sudeshna and Al-Garadi, Mohammed Ali and Sarker, Abeed},
journal={Journal of Biomedical Informatics},
volume={144},
pages={104458},
year={2023},
publisher={Elsevier}
}
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}
}