Paper: A Semi-Supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network

ACL ID P10-2055
Title A Semi-Supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

It is a fundamental and important task to ex- tract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In or- der to capture and make better use of their re- lationships in key phrase extraction, we sug- gest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated. Based on a commonly accepted assumption that the title of a document is always elaborated to reflect the content of a document and consequently key phrases tend to have close semantics to the title, we propose a novel semi-supervised key phrase extraction approach in this paper by computing the phrase importance in the se- mantic network, through which t...