2010 ParsingtoStanfordDependenciesTr

• (Cer et al., 2010) ⇒ Daniel Cer, Marie-Catherine de Marneffe, Daniel Jurafsky, and Christopher D. Manning l. (2010). “Parsing to Stanford Dependencies: Trade-offs Between Speed and Accuracy.” In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10).

Subject Headings: RelEx System; Relation Extraction System.

Cited By

• Google Scholar: ~ 162Citations (Retrieved:2019-10-17).
• Semantic Scholar: ~ 122 Citations (Retrieved:2019-10-17).

Quotes

Abstract

We investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and RelEx) as well as dependencies extracted from constituent parse trees created by phrase structure parsers (Charniak, Charniak-Johnson, Bikel, Berkeley and Stanford). We found that phrase structure parsers systematically outperform algorithms designed specifically for dependency parsing. The most accurate method for generating dependencies is the Charniak-Johnson reranking parser, with 89% (labeled) attachment F1 score. The fastest methods are Nivre, Nivre Eager, and Covington. When used with a linear classifier to make local parsing decisions, these methods can parse the entire Penn Treebank development set (section 22) in less than 10 seconds on an Intel Xeon E5520. However, this speed comes with a substantial drop in F1 score (about 76% for labeled attachment) compared to competing methods. By tuning how much of the search space is explored by the Charniak-Johnson parser, we are able to arrive at a balanced configuration that is both fast and nearly as good as the most accurate approaches

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 ParsingtoStanfordDependenciesTr	Daniel Jurafsky Marie-Catherine de Marneffe Daniel Cer Christopher D. Manning l			Parsing to Stanford Dependencies: Trade-offs Between Speed and Accuracy						2010