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首页> 外文期刊>International journal of computer processing of languages >Word Based Chinese Semantic Role Labeling with Semantic Chunking
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Word Based Chinese Semantic Role Labeling with Semantic Chunking

机译：基于词的语义分块的汉语语义角色标注

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摘要

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.
机译：最近，随着中文语义注释语料库的发展，例如在中国命题库中，中国语义角色标签（SRL）得到了增强。但是，中国SRL研究人员现在专注于移植现有的统计机器学习方法，这些方法已被证明对英语有效。在本文中，我们建立了一个基于单词的中文SRL系统，该系统与以前基于解析的系统有很大的不同。我们使用“语义分块”来表示我们的新方法。语义分块之所以被命名是因为它与语法分块相似。不同之处在于仅使用词级信息将语义分块用于识别和分类语义分块，即语义角色。基于语义分块，SRL的过程从“解析-语义角色识别-语义角色分类”更改为“语义块识别-语义块分类”。通过消除解析阶段，SRL任务可以摆脱对解析的依赖，而这是速度和精度的瓶颈。实验表明，如果分词和词性（POS）标记均正确，则基于语义分块的方法将优于先前在中文SRL上报告的最佳结果。在自动分词和POS标记上，我们的方法减少了一些。语义分块方法的最大优点是可以节省大量时间。除此之外，我们还进行了一些仅用于语义角色分类的实验。这些实验表明，仅具有词级特征，语义角色分类的性能仍然可以很高，这证明了句法结构信息并不是必不可少的。

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