Abstract：In recent years, the Chinese SRL (semantic role labeling) has aroused the intensive attention. Many SRL systems have been built on the parsing trees, in which the constituents of the sentence structure are identified and then classified. In contrast, this paper establishes a semantic chunking based method which changes the SRL task from the traditional “parsing-semantic role identification-semantic role classification” process into a simple “semantic chunk identification-semantic chunk classification” pipeline. The semantic chunking, which is named after the syntactic chunking, is used to identify the semantic chunk, namely the arguments of the verbs. Based on the semantic chunking result, the Chinese SRL can be changed into a sequence labeling problem instead of the classification problem. We apply the conditional random fields to the problem and get better performance. Along with the removal of the parsing stage, the SRL task avoids the dependence on parsing, which is always the bottleneck both of speed and precision. The experiments have shown that the outperforms of our approach previously best-reported methods on Chinese SRL with an impressive time reduction. We also show that the proposed method works much better on gold word segmentation and POS tagging than on the automatic results. Key words computer application; Chinese information processing; semantic role labeling; semantic chunking; conditional random fields; sequence labeling
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2009, Vol. 23 
