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

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,...