@InProceedings{neves-kraus:2016:OKBQA2016,
  author    = {Neves, Mariana  and  Kraus, Milena},
  title     = {BioMedLAT Corpus: Annotation of the Lexical Answer Type for Biomedical Questions},
  booktitle = {Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {49--58},
  abstract  = {Question answering (QA) systems need to provide exact answers for the questions
	that are posed to the system. However, this can only be achieved through a
	precise processing of the question. During this procedure, one important step
	is the detection of the expected type of answer that the system should provide
	by  extracting the headword of the questions and identifying its semantic type.
	We have annotated the headword and assigned UMLS semantic types to 643
	factoid/list questions from the BioASQ training data. We present statistics on
	the corpus and a preliminary evaluation in baseline experiments. We also
	discuss the challenges on both the manual annotation and the automatic
	detection of the headwords and the semantic types. We believe that this is a
	valuable resource for both training and evaluation of biomedical QA systems.
	The corpus is available at: https://github.com/mariananeves/BioMedLAT.},
  url       = {http://aclweb.org/anthology/W16-4407}
}