2010 ASemiSupervisedKeyPhraseExtract
- (Li et al., 2010) ⇒ Decong Li, Sujian Li, Wenjie Li, Wei Wang, and Weiguang Qu. (2010). “A Semi-supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network.” In: Proceedings of the ACL 2010 Conference Short Papers.
Subject Headings:
Notes
Cited By
- http://scholar.google.com/scholar?q=%22A+semi-supervised+key+phrase+extraction+approach%3A+learning+from+title+phrases+through+a+document+semantic+network%22+2010
- http://dl.acm.org/citation.cfm?id=1858842.1858897&preflayout=flat#citedby
Quotes
Abstract
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.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2010 ASemiSupervisedKeyPhraseExtract | Wei Wang Decong Li Sujian Li Wenjie Li Weiguang Qu | A Semi-supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network | 2010 |