Paper: Semantic Role Labeling of Chinese Using Transductive SVM and Semantic Heuristics

ACL ID I08-2132
Title Semantic Role Labeling of Chinese Using Transductive SVM and Semantic Heuristics
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008
Authors

Semantic Role Labeling (SRL) as a Shallow Semantic Parsing causes more and more attention recently. The shortage of manually tagged data is one of main obstacles to supervised learning, which is even serious in SRL. Transductive SVM (TSVM) is a novel semi-supervised learn- ing method special to small mount of tagged data. In this paper, we introduce an application of TSVM in Chinese SRL. To improve the performance of TSVM, some heuristics have been designed from the semantic perspective. The experiment re- sults on Chinese Propbank showed that TSVM outperforms SVM in small tagged data, and after using heuristics, it performs further better.