ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System
Anumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi, Sowmya Kamath S, Anand Kumar
Abstract
In this paper, we present three approaches for Natural Language Inference, Question Entailment Recognition and Question-Answering to improve domain-specific Information Retrieval. For addressing the NLI task, the UMLS Metathesaurus was used to find the synonyms of medical terms in given sentences, on which the InferSent model was trained to predict if the given sentence is an entailment, contradictory or neutral. We also introduce a new Extreme gradient boosting model built on PubMed embeddings to perform RQE. Further, a closed-domain Question Answering technique that uses Bi-directional LSTMs trained on the SquAD dataset to determine relevant ranks of answers for a given question is also discussed.- Anthology ID:
- W19-5059
- Volume:
- Proceedings of the 18th BioNLP Workshop and Shared Task
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 533–540
- Language:
- URL:
- https://aclanthology.org/W19-5059
- DOI:
- 10.18653/v1/W19-5059
- Cite (ACL):
- Anumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi, Sowmya Kamath S, and Anand Kumar. 2019. ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 533–540, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System (Agrawal et al., BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W19-5059.pdf
- Data
- BioASQ, MultiNLI, SQuAD