Character-level Convolutional Networks for Text Classification

Published in

Neural Information Processing Systems(2015)

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TL;DR

This article constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results in text classification.

Abstract

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

Authors

Yann LeCun

18 papers

J. Zhao

4 papers

References32 items

A statistical interpretation of term specificity and its application in retrieval

In Defense of Word Embedding for Generic Text Representation

LSTM: A Search Space Odyssey

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

Character-level Convolutional Networks for Text Classification

Published in

Neural Information Processing Systems(2015)

External Links:

Generate Graph

TL;DR

This article constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results in text classification.

Abstract

Authors

Yann LeCun

18 papers

J. Zhao

4 papers

TL;DR

Abstract

Authors

References32 items

A statistical interpretation of term specificity and its application in retrieval

In Defense of Word Embedding for Generic Text Representation

LSTM: A Search Space Odyssey

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

TL;DR

Abstract

Authors

References32 items

A statistical interpretation of term specificity and its application in retrieval

In Defense of Word Embedding for Generic Text Representation

LSTM: A Search Space Odyssey

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval

Convolutional Neural Networks for Sentence Classification

Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts

Learning Character-level Representations for Part-of-Speech Tagging

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