@InProceedings{sharma-EtAl:2018:BioNLP18,
  author    = {Sharma, Vasu  and  Kulkarni, Nitish  and  Pranavi, Srividya  and  Bayomi, Gabriel  and  Nyberg, Eric  and  Mitamura, Teruko},
  title     = {BioAMA: Towards an End to End BioMedical Question Answering System},
  booktitle = {Proceedings of the BioNLP 2018 workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {109--117},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-2312}
}