@InProceedings{sarrouti-ouatikelalaoui:2017:BioNLP17,
  author    = {Sarrouti, Mourad  and  Ouatik El Alaoui, Said},
  title     = {A Biomedical Question Answering System in BioASQ 2017},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {296--301},
  abstract  = {Question answering, the identification of short accurate answers to users
	questions,
	is a longstanding challenge widely studied over the last decades in the open
	domain. However, it still requires further efforts in the biomedical domain. In
	this paper, we describe our participation in phase B of task 5b in the 2017
	BioASQ
	challenge using our biomedical question answering system. Our system, dealing
	with four types of questions (i.e., yes/no, factoid, list, and summary), is
	based on
	(1) a dictionary-based approach for generating the exact answers of yes/no
	questions, (2) UMLS metathesaurus and term frequency metric for extracting the
	exact answers of factoid and list questions, and (3) the BM25 model and UMLS
	concepts for retrieving the ideal answers (i.e., paragraph-sized summaries).
	Preliminary
	results show that our system achieves good and competitive results in both
	exact and
	ideal answers extraction tasks as compared with the participating systems.},
  url       = {http://www.aclweb.org/anthology/W17-2337}
}