Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2020, Vol. 56 ›› Issue (1): 23-30.DOI: 10.13209/j.0479-8023.2019.094

Previous Articles     Next Articles

Representation and Recognition of Clauses Relevance Structure in Chinese Text

FENG Wenhe1,†, CHEN Yilin1, REN Yafeng2, REN Han1   

  1. 1. Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006
    2. Collaborative Innovation Center for Language Research and Service, Guangdong University of Foreign Studies, Guangzhou 510006
  • Received:2019-05-19 Revised:2019-09-21 Online:2020-01-20 Published:2020-01-20
  • Contact: FENG Wenhe, E-mail: wenhefeng(at)


冯文贺1,†, 陈伊琳1, 任亚峰2, 任函1   

  1. 1. 广东外语外贸大学语言工程与计算实验室, 广州 510006 2. 广东外语外贸大学外语研究与语言服务协同创新中心, 广州510006
  • 通讯作者: 冯文贺, E-mail: wenhefeng(at)
  • 基金资助:


The discourse structure is represented as the clause relevance structure, which can effectively describe the direct semantic association between discontinuous and cross-level clauses in a text, compared with the hierarchical discourse structure pattern such as rhetorical structure theory. Firstly, the scheme of clause relevance structure, its judgment criteria and formal constraints. The manual annotation experiments are conducted. Then, the automatic recognition of Chinese clause relevance structure is studied. On the corpus of Chinese discourse clause relevance structure we built, the best recognition accuracy is 92.70% based on the classification model, with the connectives, vocabulary and other classification features. The experimental results show that the ring-removing effect obtained by the overall sampling of corpus is better than that of independent sampling, and the features of vocabulary, clause distance and clause domain contribute greatly to the recognition. Long distance and cross sentences of clause pair are the difficulties of clause relevance recognition, but adjacent clauses and clauses in the same sentence are especially difficult to recognize as uncorrelated clauses.

Key words: clause relevance structure, discourse structure, discourse dependency structure, rhetorical structure


将篇章结构表示为小句关联结构, 与修辞结构等层次化篇章结构模式相比, 可以有效地刻画非连续和跨层级的小句之间的直接语义关联。首先, 提出篇章小句关联结构的形式表示、判断准则和形式限制, 并进行人工标注。然后, 对汉语篇章小句关联结构进行自动识别。在自建汉语篇章小句关联结构语料库上, 基于分类模型, 设计连接词和词汇等分类特征, 得到的最佳识别准确率达92.70%。实验结果表明, 语料整体取样比独立取样取得的去环效果好; 词汇、小句距离及句域等分类特征对识别的贡献较大; 远距离和跨大句是小句关联识别的难点, 但相邻小句和同一大句内的小句对的不相关识别难度更大。

关键词: 小句关联结构, 篇章结构, 篇章依存结构, 修辞结构