基于BERT-FHAN模型融合语句特征的汉语复句关系自动识别

复句关系是指复句分句之间的逻辑语义关系, 复句关系识别是对分句间语义关系的甄别, 是自然语言处理中的难点问题. 本文以有标复句为研究对象, 提出了一种BERT-FHAN模型, 该模型利用BERT模型获取词向量, 在HAN模型中融入关系词本体知识以及词性、句法依存关系、语义依存关系特征. 通过实验对提出的模型进行验证, BERT-FHAN模型取得的最高宏平均F1值和准确率分别为95.47%与96.97%, 表明了本文方法的有效性.

Automatic Recognition of Chinese Compound Sentence Relation Based on BERT-FHAN Model and Sentence Features

The relations of compound sentences refer to the logical semantic relations between the clauses. The relation recognition of compound sentences is therefore the identification of semantic relations between clauses, and it is a difficult issue in natural language processing (NLP). Taking the marked compound sentences as the research object, this study proposes a BERT-FHAN model. In this model, the BERT model is employed to obtain word vectors, and the HAN model is used to integrate the ontology knowledge of relational words, as well as the characteristics of the part of speech, syntactic dependency relations, and semantic dependency relations. The proposed model is verified by experiments, and the result indicates that the highest macro average F1 value and accuracy of the BERT-FHAN model are 95.47% and 96.97%, respectively, which demonstrates the effectiveness of the method.