  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.
