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13

ADMA
2006
Springer

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Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining

13 years 10 months ago

Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining

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This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.

Chong Huang, YongHong Tian, Tiejun Huang, Wen Gao

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ADMA 2006 | Data Mining | Effective Scoring Scheme | Scoring Scheme | Simple Frequency Summarization |

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Added	13 Jun 2010
Updated	13 Jun 2010
Type	Conference
Year	2006
Where	ADMA
Authors	Chong Huang, YongHong Tian, Tiejun Huang, Wen Gao

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