Presentation 2000/7/12
Data Mining of Sentence Structures using Relative Indexing of Vertices
Mayumi OYAMA, Takashi OKADA, Shigeki KUROSAKI,
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Abstract(in English) Data mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Corpuses are large databases and text-mining techniques are making rapid progress. If we apply the technique of data mining to discover knowledge in corpuses, we will get good results. But, to analyze corpuses with syntactic parse trees we have to treat their structures. In this paper, we show the inscription of syntactic parse tree first and we propose the data mining methods using the relative indexing of vertices for knowledge discover from corpuses. We show the distinctive structural features found between an intransitive verb"think"and a transitive verb"think".
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Keyword(in English) data mining / relative indexing / syntactic parse tree / knowledge discovery
Paper # NLC2000-19
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Committee NLC
Conference Date 2000/7/12(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Data Mining of Sentence Structures using Relative Indexing of Vertices
Sub Title (in English)
Keyword(1) data mining
Keyword(2) relative indexing
Keyword(3) syntactic parse tree
Keyword(4) knowledge discovery
1st Author's Name Mayumi OYAMA
1st Author's Affiliation Center for Information & Media Studies, Kwansei Gakuin University()
2nd Author's Name Takashi OKADA
2nd Author's Affiliation Center for Information & Media Studies, Kwansei Gakuin University
3rd Author's Name Shigeki KUROSAKI
3rd Author's Affiliation Center for Information & Media Studies, Kwansei Gakuin University
Date 2000/7/12
Paper # NLC2000-19
Volume (vol) vol.100
Number (no) 201
Page pp.pp.-
#Pages 8
Date of Issue