@InProceedings{newmangriffis-lai-foslerlussier:2017:BioNLP17,
  author    = {Newman-Griffis, Denis  and  Lai, Albert  and  Fosler-Lussier, Eric},
  title     = {Insights into Analogy Completion from the Biomedical Domain},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {19--28},
  abstract  = {Analogy completion has been a popular task in recent years for evaluating the
	semantic properties of word embeddings, but the standard methodology makes a
	number of assumptions about analogies that do not always hold, either in recent
	benchmark datasets or when expanding into other domains.  Through an analysis
	of analogies in the biomedical domain, we identify three assumptions: that of a
	Single Answer for any given analogy, that the pairs involved describe the Same
	Relationship, and that each pair is Informative with respect to the other. We
	propose modifying the standard methodology to relax these assumptions by
	allowing for multiple correct answers, reporting MAP and MRR in addition to
	accuracy, and using multiple example pairs.  We further present BMASS, a novel
	dataset for evaluating linguistic regularities in biomedical embeddings, and
	demonstrate that the relationships described in the dataset pose significant
	semantic challenges to current word embedding methods.},
  url       = {http://www.aclweb.org/anthology/W17-2303}
}