A New Cascade Algorithm Based on CRFs for Recognizing Chinese Verb-Object Collocation

摘要：

This paper proposes a new cascade algorithm based on conditional random fields. The algorithm is applied to automatic recognition of Chinese verb-object collocation, and combined with a new sequence labeling of ONIY. Experiments compare identified results under two segmentations and part-of-speech tag sets. The comprehensive experimental results show that the best performance is 90.65％ in F-score over Tsinghua Treebank , and 82.00％ in F-score over the segmentation and part-of-speech tagging scheme of Peking University. Our experiments show that the proposed algorithm can greatly improve recognition accuracy of multi-nested collocation, and play a positive role on long distance collocation.

关键词： Verb-object collocation new cascade algorithm multi-nested long distance conditional random fields

作者: Guiping ZHANG Zhichao LIU Qiaoli ZHOU Dongfeng CAI Jiao CHENG

作者单位: Knowledge Engineering ResearchCenter,Shenyang Aerospace University,Shenyang,Liaoning,China Knowledge Engineering ResearchCenter, Shenyang Aerospace University, Shenyang,Liaoning, China

会议类型: 国际会议

会议名称: The 6th International Conference on Natural Language Processing and Knowledge Engineering(第六届IEEE自然语言处理与知识工程国际会议 NLP-KE 2010)

会议地点: 北京

会议语种:英文

页码: 1-7

在线出版日期: 2010-08-21（万方平台首次上网日期，不代表论文的发表时间）