A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network
|A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network|
|Author(s)||Li D., Li S., Li W., Wang W., Qu W.|
|Published in||ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Binary relationships, Document semantics, Key-phrase, Semantic network, Semi-supervised, Wikipedia, Semantics, Computational linguistics)|
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A semi-supervised key phrase extraction approach: Learning from title phrases through a document semantic network is a 2010 conference paper written in English by Li D., Li S., Li W., Wang W., Qu W. and published in ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference.
It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest 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 semantic network, through which the influence of title phrases is propagated to the other phrases iteratively. Experimental results demonstrate the remarkable performance of this approach.
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