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(Submitted on 5 Feb 2015)
This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.
| Subjects: | Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:1502.01710 [cs.LG] |
| (or arXiv:1502.01710v1 [cs.LG] for this version) |
From: Xiang Zhang [view email]
[v1] Thu, 5 Feb 2015 20:45:19 GMT (85kb,D)
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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Text Understanding from Scratch
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原文地址：http://my.oschina.net/airship/blog/376179
