A Dependency Parser for Tweets

<p>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.</p> <p>Our dataset and parser can be found at http://www.ark.cs.cmu.edu/TweetNLP.</p

CMU: Arc-Factored, Discriminative Semantic Dependency Parsing

<p>We present an arc-factored statistical model for semantic dependency parsing, as de- fined by the SemEval 2014 Shared Task 8 on Broad-Coverage Semantic Dependency Parsing. Our entry in the open track placed second in the competition.</p