Investigating the cross-linguistic potential of VerbNet-style classification

摘要：

Verb classes which integrate a wide range of linguistic properties (Levin, 1993) have proved useful for natural language processing (NLP) applications. However, the real-world use of these classes has been limited because for most languages, no resources similar to VerbNet (Kipper- Schuler, 2005) are available. We apply a verb clustering approach developed for English to French – a language for which no such experiment has been conducted yet. Our investigation shows that not only the general methodology but also the best performing features are transferable between the languages, making it possible to learn useful VerbNet style classes for French automatically without languagespecific tuning.

作者: Lin Sun Anna Korhonen Thierry Poibeau Cedric Messiant

作者单位: Computer Laboratory University of Cambridge LaTTiCe, UMR8094 CNRS & ENS LIPN, UMR7030 CNRS & U. Paris 13

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: 1056-1064

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）