Effective Use of Word Order for Text Categorization with Convolutional Neural Networks To appear in NAACL HLT 2015.
Table 1: RCV1 data summary.
['[EMPTY]', 'label', '#train', '#test', '#class']
S3SS4SSS0Px3 RCV1: topic categorization RCV1 is a corpus of Reuters news articles as described in LYRL04 RCV1 has 103 topic categories in a hierarchy, and one document may be associated with more than one topic. Performance on this task (multi-label categorization) is known to be sensitive to thresholding strategies, which are algorithms additional to the models we would like to test. Therefore, we also experimented with single-label categorization to assign one of 55 second-level topics to each document to directly evaluate models. For this task, we used the documents from a one-month period as the test set and generated various sizes of training sets from the documents with earlier dates. As in LYRL04, we used the concatenation of the headline and text elements. Data preprocessing was the same as IMDB except that we used the stopword list provided by LYRL04 and regarded numbers as stopwords.