%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