Paper: Supervised All-Words Lexical Substitution using Delexicalized Features

ACL ID N13-1133
Title Supervised All-Words Lexical Substitution using Delexicalized Features
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

We propose a supervised lexical substitu- tion system that does not use separate clas- sifiers per word and is therefore applicable to any word in the vocabulary. Instead of learning word-specific substitution patterns, a global model for lexical substitution is trained on delexicalized (i.e., non lexical) features, which allows to exploit the power of super- vised methods while being able to general- ize beyond target words in the training set. This way, our approach remains technically straightforward, provides better performance and similar coverage in comparison to unsu- pervised approaches. Using features from lex- ical resources, as well as a variety of features computed from large corpora (n-gram counts, distributional similarity) and a ranking method based on the posterior probabil...