融合GCN远距离约束的非遗戏剧术语抽取模型构建及其应用研究

Extracting Drama Terms with GCN Long-distance Constrain

【目的】针对非遗传统戏剧提出一种效果更优的术语抽取模型,并构建出传统戏剧术语库。【方法】首先从术语类别、语义结构和文本长度上分析戏剧语言特征。根据以上语言特征,以BERT-BiLSTM-CRF模型为基础,在BERT获得的字符表示上加入词性和领域特征。之后在BiLSTM后加入图卷积网络(GCN),更好地捕捉句子中远距离词语的约束关系。【结果】融合GCN和外部特征的术语抽取模型F1值达到91.11%,比主流的BERT-BiLSTM-CRF高出1.3个百分点。【局限】仅选择百度百科、非遗官网作为实验数据来源,并未验证将模型扩展到其他来源的自由文本中的识别效率。戏剧术语中某些类别的训练集偏少,且实验数据和模型中外部特征的选择不够全面。【结论】本文根据传统戏剧语言特征,提出一种融合GCN和外部特征的戏剧术语抽取模型,构建了传统戏剧术语库,并将模型应用于术语库的扩充,为后续构建传统戏剧知识图谱打下基础。

[Objective]This study proposes a new term extraction model for the intangible heritage(traditional drama),which also helps us construct a term database.[Methods]First,we analyzed the drama language characteristics from term category,semantic structure,and text length perspectives.Then,we added part of speech and domain features to the character representation obtained by the BERT-BiLSTM-CRF model.Finally,we incorporated the graph convolutional network(GCN)to the new model and captured the constraint relationship of the distant words.[Results]The F1 value of the proposed model reached 91.11%,which was 1.3 percentage points higher than the baseline BERT-BiLSTM-CRF model.[Limitations]We only retrieved the experimental data from Baidu Baike and the official website of Intangible Cultural Heritage,which should have included more free texts from other sources,more categories of drama terms,as well as the external features.[Conclusions]The proposed model and the database for traditional drama terms will help us construct the knowledge graph for traditional drama.