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首页> 外文会议>2nd Workshop on Argumentation mining 2015 >From Argumentation Mining to Stance Classification
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From Argumentation Mining to Stance Classification

机译：从论证挖掘到立场分类

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摘要

Argumentation mining and stance classification were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. Unlike other machine learning approaches for argument tagging which often require large set of labeled data, the proposed model is minimally supervised and merely a one-to-one mapping between the pre-defined argument set and the extracted topics is required. These extracted arguments are subsequently exploited for stance classification. Additionally, a manually-annotated corpus for stance classification and argument tagging of online news comments is introduced and made available. Experiments on our collected corpus demonstrate the benefits of using topic-modeling for argument tagging. We show that using Non-Negative Matrix Factorization instead of Latent Dirich-let Allocation achieves better results for argument classification, close to the results of a supervised classifier. Furthermore, the statistical model that leverages automatically-extracted arguments as features for stance classification shows promising results.
机译：引数挖掘和姿势分类最近被引入为文本挖掘中有趣的任务。本文提出了一种基于主题建模的新颖的自变量标记框架。与其他通常需要大量标记数据的机器学习方法（通常需要大量标记数据）不同，所提议的模型受到的监督最少，并且仅需要预定义参数集和提取的主题之间的一对一映射。这些提取的参数随后被用于姿势分类。此外，引入了手动注释的语料库，用于在线新闻评论的立场分类和自变量标记。对我们收集的语料库进行的实验证明了使用主题建模进行自变量标记的好处。我们表明，使用非负矩阵分解代替潜在Dirich-let分配可以实现更好的参数分类结果，接近监督分类器的结果。此外，利用自动提取的参数作为立场分类特征的统计模型显示出可喜的结果。

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  • 来源
  • 会议地点 Denver CO(US)
  • 作者

    Parinaz Sobhani; Diana Inkpen; Stan Matwin;

  • 作者单位

    School of Electrical Engineering and Computer Science, University of Ottawa;

    School of Electrical Engineering and Computer Science, University of Ottawa;

    Faculty of Computer Science, Dalhousie University Institute of Computer Science, Polish Academy of Sciences;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
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