基于篇章主题的中文宏观篇章主次关系识别方法

篇章分析是自然语言处理领域研究的热点和重点。作为篇章分析的任务之一,篇章主次关系研究篇章的主要和次要内容,从而更好地理解和把握篇章的核心内容。该文重点研究宏观领域的中文篇章主次关系,提出了一种基于篇章主题的中文宏观篇章主次关系识别方法。该方法利用篇章单元间、篇章单元与篇章主题间的语义交互来识别主次关系,并有选择地应用篇章主题信息,有效提高了主次关系核心的识别。在中文宏观汉语篇章树库(MCDTB)上的实验结果显示,该方法优于目前性能最好的基准系统。

Recognizing Chinese Macro Discourse Nuclearity Based on Discourse Topic

Discourse analysis is a hot topic in the field of Natural Language Processing. Discourse nuclearity recognition, a subtask of discourse analysis, focuses on recognizing the main and secondary content of a discourse, to better understand and grasp its core content. This paper focuses on the task of macro Chinese discourse nulcearity recognition and proposes a recognition method based on discourse topic. This method introduces the semantic interaction between different discourse units and that between the discourse unit and its topic to identify the nuclearity. Moreover, it applies the selection mechanism of the discourse topic to further improve the performance of nuclearity recognition.Experimental results on MCDTB show that the proposed method outperforms the state-of-the-art baselines.