Vector space word representations are typically learned using only co-occurrence statistics from text corpora. Although such statistics are informative, they disregard easily accessible (and often carefully curated) information archived in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a technique to leverage both distributional and lexicon-derived evidence to obtain better representations. We run belief propagation on a word type graph constructed from word similarity information from lexicons to encourage connected (related) words to have similar representations, and also to be close to the unsupervised vectors. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, using several different underlying word vector models, we obtain substantially improved vectors and consistently outperform existing approaches of incorporating semantic knowledge in word vectors.
The Retrofit method REF refined word representations using relational information from semantic lexicons.
Retrofitting Word Vectors to Semantic Lexicons
ArXiv
ArXiv
Computer Science