Using Web-scale N-grams to Improve Base NP Parsing Performance

摘要：

We use web-scale N-grams in a base NP parser that correctly analyzes 95.4％ of the base NPs in natural text. Web-scale data improves performance. That is, there is no data like more data. Performance scales log-linearly with the number of parameters in the model (the number of unique N-grams). The web-scale N-grams are particularly helpful in harder cases, such as NPs that contain conjunctions.

作者: Emily Pitler Shane Bergsma Dekang Lin Kenneth Church

作者单位: Computer and Information Science University of Pennsylvania Department of Computing Science University of Alberta Google, Inc. Human Language Technology Center of Excellence Johns Hopkins University

会议类型: 国际会议

会议名称: The 23rd International Conference on Computational Linguistics(第23届国际计算语言学大会)

会议地点: 北京

会议语种:英文

页码: 886-894

在线出版日期: 2010-08-01（万方平台首次上网日期，不代表论文的发表时间）