tech,3-1-C04-1116,bq smaller and more robust . We present a <term> text mining method </term> for finding <term> synonymous expressions
other,8-1-C04-1116,bq text mining method </term> for finding <term> synonymous expressions </term> based on the <term> distributional
other,13-1-C04-1116,bq synonymous expressions </term> based on the <term> distributional hypothesis </term> in a set of coherent <term> corpora
lr,20-1-C04-1116,bq hypothesis </term> in a set of coherent <term> corpora </term> . This paper proposes a new methodology
measure(ment),9-2-C04-1116,bq proposes a new methodology to improve the <term> accuracy </term> of a <term> term aggregation system
tech,12-2-C04-1116,bq improve the <term> accuracy </term> of a <term> term aggregation system </term> using each author 's text as a coherent
lr,23-2-C04-1116,bq each author 's text as a coherent <term> corpus </term> . Our approach is based on the idea
other,14-3-C04-1116,bq idea that one person tends to use one <term> expression </term> for one <term> meaning </term> . According
other,17-3-C04-1116,bq one <term> expression </term> for one <term> meaning </term> . According to our assumption , most
other,11-4-C04-1116,bq assumption , most of the words with similar <term> context features </term> in each author 's <term> corpus </term>
lr,17-4-C04-1116,bq context features </term> in each author 's <term> corpus </term> tend not to be <term> synonymous expressions
other,22-4-C04-1116,bq 's <term> corpus </term> tend not to be <term> synonymous expressions </term> . Our proposed method improves the
measure(ment),5-5-C04-1116,bq . Our proposed method improves the <term> accuracy </term> of our <term> term aggregation system
tech,8-5-C04-1116,bq improves the <term> accuracy </term> of our <term> term aggregation system </term> , showing that our approach is successful
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