emnlp emnlp2013 emnlp2013-123 emnlp2013-123-reference knowledge-graph by maker-knowledge-mining
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Author: Gyorgy Szarvas ; Robert Busa-Fekete ; Eyke Hullermeier
Abstract: 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 signifi- cantly advance the state-of-the-art.
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