Learning to Parse Natural Language with Maximum Entropy Models

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文件名称：Learning to Parse Natural Language with Maximum Entropy Models
文件大小：313KB
文件格式：PDF
更新时间：2018-08-19 07:49:59
Maximum Entropy Models Parsing 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 ban 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.