W07-1311 measures , we are also working on automatic text classification . The Word Explorer , for instance
W15-5316 order to achieve proper results of automatic text classification , clearly defined classes must
P06-2081 features that can be used for automatic text classification . These experiments use essentially
W02-1820 ( 1994 ) . One central step in automatic text classification is to identify the major topics
W12-0201 for further NLP tasks such as automatic text classification . Grzegorz Kondrak 's invited
P03-1060 such as information retrieval , automatic text classification , and information extraction
W12-0207 studies ) and NLP tasks ( no - tably automatic text classification ) . 1 Introduction The present
D15-1283 the literature with regard to automatic text classification and topic prediction . Different
W15-1828 further research in the field of automatic text classification are as follows : 1 . Experiments
W13-1724 by Partial Matching ) model for automatic text classification is explored . Prediction by partial
P14-2048 Transla - tionese " dialect . Using automatic text classification methods in the field of translation
W10-3703 human or machine translation , automatic text classification or Teaching Chinese as a Foreign
J02-4005 including the study of robust automatic text classification techniques , anaphora resolution
W05-1307 tagger , co-citation analysis , and automatic text classification , we extracted a set of 6,580
J13-3009 , if more reliable and precise automatic text classification algorithms were available ( Benzineb
W14-0137 extraction , machine trans - lation , automatic text classification and summa - rization . RoWordNetLib
W14-2103 is presented in Table 3 . 3.2 Automatic text classification For all supervised learning ,
W10-0603 disambiguation , information retrieval , automatic text classification , and automatic text sum - marization
W09-0902 For example , in the field of automatic text classification , there are several databases
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