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Word Based Chinese Semantic Role Labeling with Semantic Chunking

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 word based Chinese SRL system, which is quite different from the previous parsing based ones. We use "semantic chunking" to represent our new method. Semantic chunking is named because of its similarity with syntactic chunking. The difference is that semantic chunking is used to identify and classify the semantic chunks, i.e. the semantic roles, only with the word-level information. 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, if the word segmentation and part-of-speech (POS) tagging are both correct. On the automatic word segmentation and POS tagging, our method decreases a little. The greatest advantage of semantic chunking method is that it saves a large amount of time. Besides these, we also carry out some experiments only for semantic role classification. These experiments have shown that only with the word-level features, the performance of semantic role classification can still be very high, which proves that the syntactic structural information is not indispensable.