Improvement of KEA Based on Lexical Chain

Ze Feng Li; Xiang Hui Zhao; Jin Yi; Bin  He

doi:10.4028/www.scientific.net/AMR.756-759.2999

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Improvement of KEA Based on Lexical Chain

Improvement of KEA Based on Lexical Chain

Abstract:

Keyphrases are very useful and significant for information retrieval, automatic summarizing, text clustering, etc. KEA is a traditional and classical algorithm in keyphrase automatic extraction. But it is mainly based on the statistical information without considering the semantic information. In this paper, We propose a method which combine semantic information with KEA by constructing lexical chain that based on Regets thesaurus. In our method, the semantic similarity between terms is used to construct the lexical chain, and then we use the length of the chain as a feature to build the extraction model. The experiment result shows that the performance of the system has a big improvement compare with the KEA.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

2999-3004

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.2999

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Online since:

September 2013

Authors:

Ze Feng Li, Xiang Hui Zhao, Jin Yi, Bin He

Keywords:

KEA, Keyphrases Extraction, Lexcial Chain, Semantic Similarity

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