Yang Jincai, Zheng Meng, Liu Yingliang
School of Computer Science, Central China Normal University, Wuhan, China.
School of Foreign Languages, Wuhan University of Technology, Wuhan, China.
Front Psychol. 2023 Jan 25;14:1049266. doi: 10.3389/fpsyg.2023.1049266. eCollection 2023.
Argument mining (AM), an emerging field in natural language processing (NLP), aims to automatically extract arguments and the relationships between them in texts. In this study, we propose a new method for argument mining of argumentative essays. The method generates dynamic word vectors with BERT (Bidirectional Encoder Representations from Transformers), encodes argumentative essays, and obtains word-level and essay-level features with BiLSTM (Bi-directional Long Short-Term Memory) and attention training, respectively. By integrating these two levels of features we obtain the full-text features so that the content in the essay is annotated according to Toulmin's argument model. The proposed method was tested on a corpus of 180 argumentative essays, and the precision of automatic annotation reached 69%. The experimental results show that our model outperforms existing models in argument mining. The model can provide technical support for the automatic scoring system, particularly on the evaluation of the content of argumentative essays.
论证挖掘（AM）是自然语言处理（NLP）中一个新兴的领域，旨在自动提取文本中的论点及其之间的关系。在本研究中，我们提出了一种用于议论文论证挖掘的新方法。该方法使用BERT（来自Transformer的双向编码器表示）生成动态词向量，对议论文进行编码，并分别通过双向长短期记忆网络（BiLSTM）和注意力训练获得词级和篇章级特征。通过整合这两个层次的特征，我们获得了全文特征，从而根据图尔敏论证模型对文章内容进行标注。该方法在一个包含180篇议论文的语料库上进行了测试，自动标注的精确率达到了69%。实验结果表明，我们的模型在论证挖掘方面优于现有模型。该模型可以为自动评分系统提供技术支持，特别是在议论文内容的评估方面。
