We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Our experiments show that the parser achieves over 80% unlabeled attachment accuracy on our new, high-quality test set and measure the benefit of our contributions.Our dataset and parser can be found at http://www.ark.cs.cmu.edu/TweetNLP.
|Title of host publication||Proceedings of the Conference on Empirical Methods in Natural Language Processing|
|Place of Publication||Doha, Qatar|
|Publisher||Association for Computational Linguistics|
|Number of pages||12|
|Publication status||Published - 1 Oct 2014|