Paper: Measuring Term Informativeness in Context

ACL ID N13-1026
Title Measuring Term Informativeness in Context
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Measuring term informativeness is a funda- mental NLP task. Existing methods, mostly based on statistical information in corpora, do not actually measure informativeness of a term with regard to its semantic context. This pa- per proposes a new lightweight feature-free approach to encode term informativeness in context by leveraging web knowledge. Given a term and its context, we model context- aware term informativeness based on semantic similarity between the context and the term?s most featured context in a knowledge base, Wikipedia. We apply our method to three ap- plications: core term extraction from snippets (text segment), scientific keywords extraction (paper), and back-of-the-book index genera- tion (book). The performance is state-of-the- art or close to it for each application,...