| lr,17-4-C04-1116,bq | context features </term> in each author 's <term> | corpus | </term> tend not to be <term> synonymous expressions | #6175 According to our assumption, most of the words with similar context features in each author'scorpus tend not to be synonymous expressions. |
| lr,20-1-C04-1116,bq | hypothesis </term> in a set of coherent <term> | corpora | </term> . This paper proposes a new methodology | #6112 We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherentcorpora. |
| lr,23-2-C04-1116,bq | each author 's text as a coherent <term> | corpus | </term> . Our approach is based on the idea | #6137 This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherentcorpus. |
| measure(ment),5-5-C04-1116,bq | . Our proposed method improves the <term> | accuracy | </term> of our <term> term aggregation system | #6188 Our proposed method improves theaccuracy of our term aggregation system, showing that our approach is successful. |
| measure(ment),9-2-C04-1116,bq | proposes a new methodology to improve the <term> | accuracy | </term> of a <term> term aggregation system | #6123 This paper proposes a new methodology to improve theaccuracy of a term aggregation system using each author's text as a coherent corpus. |
| other,11-4-C04-1116,bq | assumption , most of the words with similar <term> | context features | </term> in each author 's <term> corpus </term> | #6169 According to our assumption, most of the words with similarcontext features in each author's corpus tend not to be synonymous expressions. |
| other,13-1-C04-1116,bq | synonymous expressions </term> based on the <term> | distributional hypothesis | </term> in a set of coherent <term> corpora | #6105 We present a text mining method for finding synonymous expressions based on thedistributional hypothesis in a set of coherent corpora. |
| other,14-3-C04-1116,bq | idea that one person tends to use one <term> | expression | </term> for one <term> meaning </term> . According | #6153 Our approach is based on the idea that one person tends to use oneexpression for one meaning. |
| other,17-3-C04-1116,bq | one <term> expression </term> for one <term> | meaning | </term> . According to our assumption , most | #6156 Our approach is based on the idea that one person tends to use one expression for onemeaning. |
| other,22-4-C04-1116,bq | 's <term> corpus </term> tend not to be <term> | synonymous expressions | </term> . Our proposed method improves the | #6180 According to our assumption, most of the words with similar context features in each author's corpus tend not to besynonymous expressions. |
| other,8-1-C04-1116,bq | text mining method </term> for finding <term> | synonymous expressions | </term> based on the <term> distributional | #6100 We present a text mining method for findingsynonymous expressions based on the distributional hypothesis in a set of coherent corpora. |
| 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 | #6126 This paper proposes a new methodology to improve the accuracy of aterm aggregation system using each author's text as a coherent corpus. |
| tech,3-1-C04-1116,bq | smaller and more robust . We present a <term> | text mining method | </term> for finding <term> synonymous expressions | #6095 We present atext mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. |
| tech,8-5-C04-1116,bq | improves the <term> accuracy </term> of our <term> | term aggregation system | </term> , showing that our approach is successful | #6191 Our proposed method improves the accuracy of ourterm aggregation system, showing that our approach is successful. |
