@InProceedings{finley-farmer-pakhomov:2017:starSEM,
  author    = {Finley, Gregory  and  Farmer, Stephanie  and  Pakhomov, Serguei},
  title     = {What Analogies Reveal about Word Vectors and their Compositionality},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1--11},
  abstract  = {Analogy completion via vector arithmetic has become a common means of
	demonstrating the compositionality of word embeddings. Previous work have shown
	that this strategy works more reliably for certain types of analogical word
	relationships than for others, but these studies have not offered a convincing
	account for why this is the case. We arrive at such an account through an
	experiment that targets a wide variety of analogy questions and defines a
	baseline condition to more accurately measure the efficacy of our system. We
	find that the most reliably solvable analogy categories involve either 1) the
	application of a morpheme with clear syntactic effects, 2) male--female
	alternations, or 3) named entities. These broader types do not pattern cleanly
	along a syntactic--semantic divide. We suggest instead that their commonality
	is distributional, in that the difference between the distributions of two
	words in any given pair encompasses a relatively small number of word types.
	Our study offers a needed explanation for why analogy tests succeed and fail
	where they do and provides nuanced insight into the relationship between word
	distributions and the theoretical linguistic domains of syntax and semantics.},
  url       = {http://www.aclweb.org/anthology/S17-1001}
}