Paper: Learning to Rank Lexical Substitutions

ACL ID D13-1198
Title Learning to Rank Lexical Substitutions
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

The problem to replace a word with a syn- onym that fits well in its sentential context is known as the lexical substitution task. In this paper, we tackle this task as a supervised ranking problem. Given a dataset of target words, their sentential contexts and the poten- tial substitutions for the target words, the goal is to train a model that accurately ranks the candidate substitutions based on their contex- tual fitness. As a key contribution, we cus- tomize and evaluate several learning-to-rank models to the lexical substitution task, includ- ing classification-based and regression-based approaches. On two datasets widely used for lexical substitution, our best models signifi- cantly advance the state-of-the-art.