Date of Original Version
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Copyright 2015 Association for Computational Linguistics
Abstract or Description
Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representations, and it makes no assumptions about how the input vectors were constructed. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, we obtain substantial improvements starting with a variety of word vector models. Our refinement method outperforms prior techniques for incorporating semantic lexicons into word vector training algorithms.
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1606-1615.