1999 LearningToParseNLwithMaxEnt

Subject Headings: Supervised NL Parsing, Maximum-Entropy Model.

Notes

(Charniak, 2000) ⇒ Eugene Charniak. (2000). “A Maximum-Entropy-Inspired Parser.” In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference (NAACL 2000)

This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific to the parsing problem, while the linguistic hints that it uses to learn can be specified concisely. It therefore requires a minimal amount of human effort and linguistic knowledge for its construction. In practice, the running time of the parser on a test sentence is linear with respect to the sentence length. We also demonstrate that the parser can train from other domains without modification to the modeling framework or the linguistic hints it uses to learn. Furthermore, this paper shows that research into rescoring the top 20 parses returned by the parser might yield accuracies dramatically higher than the state-of-the-art.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 LearningToParseNLwithMaxEnt	Adwait Ratnaparkhi			Learning to Parse Natural Language with Maximum Entropy		Machine Learning (ML) Subject Area	http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.4133	10.1023/A:1007502103375		1999