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. |
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,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,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. |
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,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. |
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. |