no code implementations • EMNLP (LAW, DMR) 2021 • Ishani Mondal, Kalika Bali, Mohit Jain, Monojit Choudhury, ASHISH SHARMA, Evans Gitau, Jacki O’Neill, Kagonya Awori, Sarah Gitau
In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South.
no code implementations • EMNLP (ClinicalNLP) 2020 • Tirthankar Dasgupta, Ishani Mondal, Abir Naskar, Lipika Dey
Eligibility criteria in the clinical trials specify the characteristics that a patient must or must not possess in order to be treated according to a standard clinical care guideline.
no code implementations • LREC 2022 • Ishani Mondal, Kalika Bali, Mohit Jain, Monojit Choudhury, Jacki O’Neill, Millicent Ochieng, Kagnoya Awori, Keshet Ronen
In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV.
no code implementations • 24 May 2023 • Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
Learning template based information extraction from documents is a crucial yet difficult task.
1 code implementation • 19 Feb 2023 • Ankan Mullick, Ishani Mondal, Sourjyadip Ray, R Raghav, G Sai Chaitanya, Pawan Goyal
Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare.
1 code implementation • 20 Nov 2022 • Shivani Kumar, Ishani Mondal, Md Shad Akhtar, Tanmoy Chakraborty
To this end, we explore the task of Sarcasm Explanation in Dialogues, which aims to unfold the hidden irony behind sarcastic utterances.
5 code implementations • 16 Apr 2022 • Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi, Daniel Khashabi
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
no code implementations • COLING 2022 • Ishani Mondal, Kabir Ahuja, Mohit Jain, Jacki O Neil, Kalika Bali, Monojit Choudhury
The COVID-19 pandemic has brought out both the best and worst of language technology (LT).
no code implementations • 5 Oct 2021 • Ishani Mondal, Procheta Sen, Debasis Ganguly
In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e. g., race, gender etc.).
no code implementations • 2 Jun 2021 • Ishani Mondal, Yufang Hou, Charles Jochim
This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers.
1 code implementation • NAACL 2021 • Ishani Mondal
Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis.
no code implementations • AACL (knlp) 2020 • Ishani Mondal
Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain.
no code implementations • 21 Dec 2020 • Ishani Mondal
In this paper, we explore how to incorporate domain knowledge of the biomedical entities (such as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for predicting Drug-Drug Interaction from textual corpus.
no code implementations • WS 2019 • Ishani Mondal, Sukannya Purkayastha, Sudeshna Sarkar, Pawan Goyal, Jitesh Pillai, Amitava Bhattacharyya, Mahanandeeshwar Gattu
Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB).
no code implementations • 2 Sep 2020 • Ishani Mondal, Debasis Ganguly
An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner.