Argument Component Classification by Relation Identification by Neural Network and TextRank
Argument Component Classification by Relation Identification by Neural Network and TextRank
复制标题
通过神经网络和 TextRank 的关系识别对论元成分进行分类
DOI：
10.18653/v1/w19-4510
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
K. Yamaguchi
中科院分区：
文献类型：
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作者：
M. Deguchi;K. Yamaguchi
近年来，从论文和辩论等非结构化文档中自动提取论证结构的论证挖掘越来越受到关注。对于论证挖掘应用程序，论证组件分类是一个重要的子任务。现有的方法可以分为有监督方法和无监督方法。监督文档分类使用单个句子执行分类，而不依赖于整个文档。另一方面，无监督文档分类的优点是能够使用整个文档，但这些方法的准确率不是那么高。在本文中，我们提出了一种论元成分分类方法，该方法结合神经网络的关系识别和 TextRank 来整合关系信息（即关系的强度）。该方法可以通过在语料库上采用监督学习来使用特定于论证的知识，同时保持使用整个文档的优势。对两个语料库（一个由学生论文组成，另一个由维基百科文章组成）的实验表明了该方法的有效性。
In recent years, argumentation mining, which automatically extracts the structure of argumentation from unstructured documents such as essays and debates, is gaining attention. For argumentation mining applications, argument-component classification is an important subtask. The existing methods can be classified into supervised methods and unsupervised methods. Supervised document classification performs classification using a single sentence without relying on the whole document. On the other hand, unsupervised document classification has the advantage of being able to use the whole document, but accuracy of these methods is not so high. In this paper, we propose a method for argument-component classification that combines relation identification by neural networks and TextRank to integrate relation informations (i.e. the strength of the relation). This method can use argumentation-specific knowledge by employing a supervised learning on a corpus while maintaining the advantage of using the whole document. Experiments on two corpora, one consisting of student essay and the other of Wikipedia articles, show the effectiveness of this method.