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Specificity is important for extracting collocations, keyphrases, multi-wordand index terms [Newman et al. 2012]. It is also useful for tagging, ontologyconstruction [Ryu and Choi 2006], and automatic summarization of documents[Louis and Nenkova 2011, Chali and Hassan 2012]. Term frequency andinverse-document frequency (TF-IDF) are typically used to do this, but fail totake advantage of the semantic relationships between terms [Church and Gale1995]. The result is that general idiomatic terms are mistaken for specificterms. We demonstrate use of relational data for estimation of termspecificity. The specificity of a term can be learned from its distribution ofrelations with other terms. This technique is useful for identifying relevantwords or terms for other natural language processing tasks.

Natural Language Feature Selection via Cooccurrence