Abstract

Many NLP systems use dependency parsers as critical components. Jonit learning parsers usually achieve better parsing accuracies than two-stage methods. However, classical joint parsing algorithms significantly increase computational complexity, which makes joint learning impractical. In this paper, we proposed an efficient dependency parsing algorithm that is capable of capturing multiple edge-label features, while maintaining low computational complexity. We evaluate our parser on 14 different languages. Our parser consistently obtains more accurate results than three baseline systems and three popular, off-the-shelf parsers.

Metadata

publication

Proceedings of the 2015 Conference on Empirical Methods in Natural Language …, 2015

year

2015

publication date

2015/9/16

authors

Xuezhe Ma, Eduard Hovy

link

https://aclanthology.org/D15-1154.pdf

resource_link

https://aclanthology.org/D15-1154.pdf

conference

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)

pages

1322--1328

publisher

Association for Computational Linguistics