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arXiv 1605.034812016-05-17cs.LGcs.CL

Tweet2Vec: Character-Based Distributed Representations for Social Media

Carnegie Mellon University（卡内基梅隆大学）

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

备注：: 6 pages, 2 figures, 4 tables, accepted as conference paper at ACL 2016

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英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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