Bag of Tricks for Efficient Text Classification

Published in

Conference of the European Chapter of the Assoc...(2016)

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TL;DR

A simple and efficient baseline for text classification is explored that shows that the fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation.

Abstract

This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.

Authors

Edouard Grave

12 papers

Piotr Bojanowski

15 papers

Armand Joulin

18 papers

References31 items

Very Deep Convolutional Networks for Text Classification

Very Deep Convolutional Networks for Natural Language Processing

Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers

Character-level Convolutional Networks for Text Classification

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Bag of Tricks for Efficient Text Classification

Published in

Conference of the European Chapter of the Assoc...(2016)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Edouard Grave

12 papers

Piotr Bojanowski

15 papers

Armand Joulin

18 papers

References31 items

Very Deep Convolutional Networks for Text Classification

Very Deep Convolutional Networks for Natural Language Processing

Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers

Character-level Convolutional Networks for Text Classification

Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

Tomas Mikolov

9 papers

Improving Distributional Similarity with Lessons Learned from Word Embeddings

Large-Scale Deep Learning on the YFCC100M Dataset

Text Understanding from Scratch

#TagSpace: Semantic Embeddings from Hashtags

Convolutional Neural Networks for Sentence Classification

Distributed Representations of Sentences and Documents

Efficient Estimation of Word Representations in Vector Space

Baselines and Bigrams: Simple, Good Sentiment and Topic Classification

Strategies for training large scale neural network language models

A reliable effective terascale linear learning system

Feature hashing for large scale multitask learning

Latent semantic analysis

Opinion Mining and Sentiment Analysis

A unified architecture for natural language processing: deep neural networks with multitask learning

LIBLINEAR: A Library for Large Linear Classification

A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering

A neural probabilistic language model

Classes for fast maximum entropy training

Text Categorization with Support Vector Machines: Learning with Many Relevant Features

Dimensions of meaning

Learning internal representations by error propagation

Opinion Mining and Sentiment Analysis

Yfcc100m: The new data in multimedia research

A comparison of event models for naive bayes text classification

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence WSABIE: Scaling Up To Large Vocabulary Image Annotation

naive bayes text classiﬁcation

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/1607.01759

Venue Information

Name

Conference of the European Chapter of the Association for Computational Linguistics

Type

conference

URL

https://www.aclweb.org/anthology/venues/eacl/

Alternate Names

Conf Eur Chapter Assoc Comput Linguistics
EACL