自然语言显式命题自动识别和解析方法

Automatic Recognition and Analysis of Explicit Propositions in Natural Language

自然语言中包含很多显式命题,正确理解这些命题是理解文本信息的关键。正确识别显式命题并解析其中的关键成分有助于理清语言中的逻辑关系、辅助自然语言理解。该文基于百度百科数据构建了自然语言显式命题标注数据集,并提出两个研究任务:自然语言显式命题自动识别和命题关键成分解析。其中,显式命题自动识别任务判断一个自然语言句子是否为命题;显式命题关键成分解析任务从已获取的命题中解析出支撑该命题成立的关键成分。针对任务一,构建基于BERT的二分类模型;针对任务二,构建基于BERT-BiLSTM-CRF的序列标注模型。实验结果表明,模型在任务一的正确率达到74.95%,超过基线模型15.30%;在任务二的F值达到90.74%,超过基线模型17.69%。该文为下一步研究提供了可靠的标注数据集和基线方法。

There are a large number of explicit propositions in natural language, which contain the vital information about the text. Recognizing the proposition and analyzing the essential ingredients in propositions can reveal the logic behind the text and contribute to better natural language understanding. Based on the Baidupedia, we build an explicit proposition corpus and then propose two tasks: the automatic explicit proposition recognition and the essential explicit proposition ingredients analysis. Further, we construct a classification model based on BERT for the first task to determine whether a sentence is a proposition or not. And for the task two, we construct a sequence labeling model based on BERT-BiLSTM-CRF to select the essential ingredients in the propositions. The experimental results show that we achieve an accuracy of 74.95%(15.30% increase over the baseline) in task one, and a F-value of 90.74%(17.69% better than the baseline) in task two.