Abstract—

The article considers an indicator approach to the extraction of arguments found in popular science literature. The types of argumentation indicators and their relationship to the set of discourse markers are presented, and a method of compiling an indicator dictionary is given. An approach to the use of argumentation indicators in deep learning methods based on the analysis of indicator contexts is proposed. To build a training sample for each indicator, the main statement is extracted, as well as the left and right contexts usually represented by neighboring sentences. For each set the presence of argumentation is marked up according to the corpus annotation. To build the classifier, a list of 143 indicators of argumentation and a marked-up corpus including 162 articles of the popular science genre hosted on the ArgNetBank Studio web platform were used. In total, about 4600 training contexts were obtained on the basis of the corpus. The results of the experiments on argument mining showed the best performance of the classifier based on indicators.

中文翻译：

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基于指标法的论证提取

摘要-

本文考虑了一种提取科普文献中论点的指标方法。给出了论证指示符的类型及其与话语标记集的关系，并给出了编制指示符词典的方法。提出了一种基于指标上下文分析的深度学习方法中论证指标的使用方法。为了为每个指标构建训练样本，需要提取主要语句以及通常由相邻句子表示的左右上下文。对于每个集合，论证的存在根据语料库注释进行标记。为了构建分类器，使用了包含 143 个论证指标的列表以及 ArgNetBank Studio 网络平台上托管的包含 162 篇科普类型文章的标记语料库。基于该语料库总共获得了约 4600 个训练上下文。参数挖掘的实验结果表明基于指标的分类器具有最佳性能。