D15-1283 years , effective methods for topic classification are greatly needed . Current
H01-1011 to the KNN algorithm applied to topic classification . It searches the training document
D15-1283 view that are most useful for our topic classification task . The larger the word ,
D15-1283 our co-training based model for topic classification of research papers . In our model
D15-1283 data in order to make accurate topic classification . Semi-supervised methods essentially
E03-1056 In Section 2 we argued that for topic classification only positive evidence , i.e.
D15-1283 unlabeled data yield accurate topic classification of research articles . The rest
D15-1283 present a co-training approach to topic classification of research papers that effectively
D10-1045 transcripts . Spoken document topic classification has been an application of particular
D10-1045 baseline methods . After all , topic classification without ASR is still a difficult
D09-1052 industry , and region . We performed topic classification with the four general topics
D15-1242 embeddings perform better on document topic classification than similarity embeddings ,
D15-1283 effective and efficient methods for topic classification of research articles in order
D15-1283 in a co-training framework for topic classification of research papers . 3 Data The
D15-1283 that has the potential to improve topic classification . For example , in a citation
D15-1242 relatedness embeddings at a document topic classification task . Lastly , we varied the
E03-1056 from vizsla , we argue that in topic classification only positive evidence matters
D15-1242 linear kernel and evaluate document topic classification accuracy using ten-fold cross-validation
D15-1130 improvements in sentiment anal - ysis , topic classification and trait detection . None of
D15-1283 av - eraged . 4 Co-Training for Topic Classification Blum and Mitchell ( 1998 ) proposed
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