@InProceedings{mcinnes-pedersen:2017:BioNLP17,
  author    = {McInnes, Bridget  and  Pedersen, Ted},
  title     = {Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second--Order Vectors},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {107--116},
  abstract  = {Vector space methods that measure semantic similarity and relatedness often
	rely on distributional information such as co--occurrence frequencies or
	statistical measures of association to weight the importance of particular
	co--occurrences. In this paper, we extend these methods by incorporating a
	measure of semantic similarity based on a human curated taxonomy into a
	second--order vector representation. This results in a measure of semantic
	relatedness that combines both the contextual  information available in a
	corpus--based vector space representation with the semantic knowledge found in
	a biomedical ontology. Our results show that incorporating semantic similarity
	into a second order co-occurrence matrices improves correlation with human
	judgments for both similarity and relatedness, and that our method compares
	favorably to various different word embedding methods that have recently been
	evaluated on the same reference standards we have used.},
  url       = {http://www.aclweb.org/anthology/W17-2313}
}