A dependency parser for tweets

DMCA

A dependency parser for tweets

Cached

Download Links

by Lingpeng Kong , Nathan Schneider , Swabha Swayamdipta , Archna Bhatia , Chris Dyer , Noah A. Smith

Venue:	In Proceedings of the Conference on Empirical Methods in Natural Language Processing
Citations:	18 - 2 self

BibTeX

@INPROCEEDINGS{Kong_adependency,
    author = {Lingpeng Kong and Nathan Schneider and Swabha Swayamdipta and Archna Bhatia and Chris Dyer and Noah A. Smith},
    title = {A dependency parser for tweets},
    booktitle = {In Proceedings of the Conference on Empirical Methods in Natural Language Processing},
    year = {}
}

OpenURL

Abstract

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 contribu-tions. Our dataset and parser can be found at

Keyphrases

dependency parser new syntactic annotation out-of-domain penn treebank data high-quality test set english tweet new dependency parser new approach several contribution statistical parsing algorithm unlabeled attachment accuracy

DMCA