宏观篇章分析旨在分析相邻段落或段落群之间的语义联系,是自然语言处理领域其他任务的工作基础。该文研究了宏观篇章分析中的关系识别问题,提出了一个宏观篇章关系识别模型。该模型利用基于词向量的宏观篇章语义表示方法和适用于宏观篇章关系识别的结构特征,从两个层面提高了模型分辨宏观篇章关系的能力。在汉语宏观篇章树库(MCDTB)上的实验表明,该模型在大类分类中F1值达到了68.22%,比基准系统提升了4.17%。
Abstract
The macro discourse analysis aims to analyze the semantic relations between adjacent paragraphs or paragraph groups, which is a less-addressed fundamental work of in the field of natural language processing. This paper proposes a classification model to decide the relation in macro discourse-level. This model introduces a distribute representation of macro discourse semantics on word vectors and a set of structure features to improve the performance. The experimental results on the Macro Chinese Discourse Tree Bank (MCDTB) show that the F1 value of our model reaches 68.22%, achieving 4.17% improvement.
关键词
宏观篇章关系识别 /
宏观篇章结构特征 /
宏观篇章语义表示
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Key words
macro discourse-level relation classification /
structure features of macro discourse /
representation of marco discourse
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参考文献
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脚注
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基金
国家自然科学基金(61772354,61773276,61836007);国防科技先导计划(17-ZLXDXX-02-06-02-04)
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