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Text Understanding from Scratch

2015-02-05 · Xiang Zhang, Yann Lecun

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.

📄 PDF Abstract BibTeX arXiv:1502.01710

Code (2)

sunginmkone/AWS_Training_NLP torch
zhangxiangxiao/Crepe torch

Tasks

General ClassificationSentiment Analysis

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