Paper: Discriminative Training for Near-Synonym Substitution

ACL ID C10-1141
Title Discriminative Training for Near-Synonym Substitution
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

Near-synonyms are useful knowledge re- sources for many natural language applica- tions such as query expansion for information retrieval (IR) and paraphrasing for text gen- eration. However, near-synonyms are not nec- essarily interchangeable in contexts due to their specific usage and syntactic constraints. Accordingly, it is worth to develop algorithms to verify whether near-synonyms do match the given contexts. In this paper, we consider the near-synonym substitution task as a classifica- tion task, where a classifier is trained for each near-synonym set to classify test examples into one of the near-synonyms in the set. We also propose the use of discriminative training to improve classifiers by distinguishing posi- tive and negative features for each near- synonym. Experim...