Learning to Rank Lexical Substitutions
György Szarvas, Róbert Busa‐Fekete, Eyke Hüllermeier · 2013
The problem to replace a word with a synonym 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 potential substitutions for the target words, the goal is to train a model that accurately ranks the candidate substitutions based on their contextual fitness.As a key contribution, we customize and evaluate several learning-to-rank models to the lexical substitution task, including classification-based and regression-based approaches.On two datasets widely used for lexical substitution, our best models significantly advance the state-of-the-art.