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arXiv · 1605.03481

Tweet2Vec: Character-Based Distributed Representations for Social Media

Bhuwan Dhingra·Zhong Zhou·Dylan Fitzpatrick·Michael Muehl·William W. Cohen
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Abstract

Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many out-of-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available.

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Bhuwan Dhingra, Zhong Zhou, Dylan Fitzpatrick, Michael Muehl, William W. Cohen. 2016-05-17. Tweet2Vec: Character-Based Distributed Representations for Social Media. https://arxiv.org/abs/1605.03481

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