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    <abstract>In this paper, we present a novel Biomedical Question Answering system, BioAMA: “Biomedical Ask Me Anything” on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed ‘ideal’ answers. For summary-type questions, we combine effective IR-based techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model). Additionally, we propose a novel NLI-based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised or unsupervised techniques.</abstract>
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%0 Conference Proceedings
%T BioAMA: Towards an End to End BioMedical Question Answering System
%A Sharma, Vasu
%A Kulkarni, Nitish
%A Pranavi, Srividya
%A Bayomi, Gabriel
%A Nyberg, Eric
%A Mitamura, Teruko
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the BioNLP 2018 workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sharma-etal-2018-bioama
%X In this paper, we present a novel Biomedical Question Answering system, BioAMA: “Biomedical Ask Me Anything” on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed ‘ideal’ answers. For summary-type questions, we combine effective IR-based techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model). Additionally, we propose a novel NLI-based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised or unsupervised techniques.
%R 10.18653/v1/W18-2312
%U https://aclanthology.org/W18-2312
%U https://doi.org/10.18653/v1/W18-2312
%P 109-117

Markdown (Informal)

[BioAMA: Towards an End to End BioMedical Question Answering System](https://aclanthology.org/W18-2312) (Sharma et al., BioNLP 2018)

• BioAMA: Towards an End to End BioMedical Question Answering System (Sharma et al., BioNLP 2018)

ACL

• Vasu Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, and Teruko Mitamura. 2018. BioAMA: Towards an End to End BioMedical Question Answering System. In Proceedings of the BioNLP 2018 workshop, pages 109–117, Melbourne, Australia. Association for Computational Linguistics.