Presentation 2007/9/28
A Bootstrapping Approach to Semantic Knowledge Acquisition using Query Logs
Mamoru KOMACHI, Hisami SUZUKI,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) We propose a bootstrapping method for learning semantic categories of words from query logs of web search. Our method is based on the Espresso algorithm for extracting binary relations, but makes important modifications for handling the query log data for the task of acquiring semantic categories. We present experimental results comparing our method with two state-of-the-art semi-supervised lexical knowledge extraction systems using Japanese query log data, and show that our method achieves higher precision, runs faster and collects more meaningful contextual patterns for characterizing the categories than the previously proposed methods. We also show that the proposed method offers an additional advantage for knowledge acquisition for Asian language for which word segmentation is an issue, as the method utilizes no prior knowledge of word segmentation, and is able to harvest new terms with correct word segmentation.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Query Log / Semantic Knowledge / Named Entity / Semi-supervised Learning
Paper # NLC2007-31
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Conference Information
Committee NLC
Conference Date 2007/9/28(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) A Bootstrapping Approach to Semantic Knowledge Acquisition using Query Logs
Sub Title (in English)
Keyword(1) Query Log
Keyword(2) Semantic Knowledge
Keyword(3) Named Entity
Keyword(4) Semi-supervised Learning
1st Author's Name Mamoru KOMACHI
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name Hisami SUZUKI
2nd Author's Affiliation Microsoft Research One Microsoft Way
Date 2007/9/28
Paper # NLC2007-31
Volume (vol) vol.107
Number (no) 246
Page pp.pp.-
#Pages 6
Date of Issue