Fast semantic role labeling for chinese based on semantic chunking

Abstract

Recently, with the development of Chinese semantically annotated corpora, e.g. the Chinese Proposition Bank, the Chinese semantic role labeling (SRL) has been boosted. However, the Chinese SRL researchers now focus on the transplant of existing statistical machine learning methods which have been proven to be effective on English. In this paper, we have established a semantic chunking based method which is different from the traditional ones. Semantic chunking is named because of its similarity with syntactic chunking. The difference is that semantic chunking is used to identify the semantic chunks, i.e. the semantic roles. Based on semantic chunking, the process of SRL is changed from "parsing - semantic role identification - semantic role classification", to "semantic chunk identification - semantic chunk classification". With the elimination of the parsing stage, the SRL task can get rid of the dependency on parsing, which is the bottleneck both of speed and precision. The experiments have shown that the semantic chunking based method outperforms previously best-reported results on Chinese SRL and saves a large amount of time. © 2009 Springer Berlin Heidelberg.