Effective Use of Word Order for Text Categorization with Convolutional Neural Networks To appear in NAACL HLT 2015.
Table 2: Error rate (%) comparison with bag-of-n-gram-based methods. Sentiment classification on IMDB and Elec (25K training documents) and 55-way topic categorization on RCV1 (16K training documents). ‘(30K)’ indicates that the 30K most frequent n-grams were used, and ‘(all)’ indicates that all the n-grams (up to 5M) were used. CNN used the 30K most frequent words.
['methods', 'IMDB', 'Elec', 'RCV1']
The first thing to note is that on all the datasets, the best-performing CNN outperforms the baseline methods, which demonstrates the effectiveness of our approach.