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measures , we are also working on
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automatic text classification
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. The Word Explorer , for instance
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W15-5316 |
order to achieve proper results of
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automatic text classification
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, clearly defined classes must
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P06-2081 |
features that can be used for
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automatic text classification
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. These experiments use essentially
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W02-1820 |
( 1994 ) . One central step in
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automatic text classification
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is to identify the major topics
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W12-0201 |
for further NLP tasks such as
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automatic text classification
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. Grzegorz Kondrak 's invited
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P03-1060 |
such as information retrieval ,
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automatic text classification
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, and information extraction
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W12-0207 |
studies ) and NLP tasks ( no - tably
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automatic text classification
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) . 1 Introduction The present
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D15-1283 |
the literature with regard to
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automatic text classification
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and topic prediction . Different
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W15-1828 |
further research in the field of
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automatic text classification
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are as follows : 1 . Experiments
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W13-1724 |
by Partial Matching ) model for
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automatic text classification
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is explored . Prediction by partial
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P14-2048 |
Transla - tionese " dialect . Using
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automatic text classification
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methods in the field of translation
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W10-3703 |
human or machine translation ,
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automatic text classification
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or Teaching Chinese as a Foreign
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J02-4005 |
including the study of robust
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automatic text classification
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techniques , anaphora resolution
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W05-1307 |
tagger , co-citation analysis , and
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automatic text classification
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, we extracted a set of 6,580
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J13-3009 |
, if more reliable and precise
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automatic text classification
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algorithms were available ( Benzineb
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W14-0137 |
extraction , machine trans - lation ,
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automatic text classification
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and summa - rization . RoWordNetLib
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W14-2103 |
is presented in Table 3 . 3.2
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Automatic text classification
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For all supervised learning ,
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W10-0603 |
disambiguation , information retrieval ,
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automatic text classification
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, and automatic text sum - marization
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W09-0902 |
For example , in the field of
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automatic text classification
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, there are several databases
|