Blog
About

10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

      Preprint

      ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing of Conneau et al. The shallow word-level CNN uses more parameters and therefore requires more storage than the deep character-level CNN; however, the shallow word-level CNN computes much faster.

          Related collections

          Author and article information

          Journal
          2016-08-31
          Article
          1609.00718

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          cs.CL cs.LG stat.ML

          Theoretical computer science, Machine learning, Artificial intelligence

          Comments

          Comment on this article