A Fact-checking Assistant System for Textual Documents*

Tomoya Furuta, Yumiko Suzuki
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引用次数: 1

Abstract

This paper proposes a system for identifying which parts of textual documents the editors should do fact-checking. Using our system, we can reduce editors’ time and efforts by identifying descriptions that need fact-checking. To accomplish this purpose, we construct a machine-learning-based classifier of sentences, which classifies a part of documents into four classes: according to the necessity of fact-checking. We assume that there are typical descriptions which contain misinformation. Therefore, if we collect the documents and their revised documents, and labels whether their revisions are corrections or not, we can construct the classifier by learning the dataset. To construct this classifier, we build a dataset that includes a set of sentences which are revised more than once, from Wikipedia edit history. The labels indicate the degree of sentence corrections by editors. We develop a Web-based system for demonstrating our proposed approach. When we input texts, the system predicts which parts of the texts the editors should re-confirm the facts.
文本文件事实核查助理系统*
本文提出了一个系统,用于识别编辑应该对文本文件的哪些部分进行事实核查。使用我们的系统,我们可以通过识别需要事实核查的描述来减少编辑的时间和努力。为了实现这一目的,我们构建了一个基于机器学习的句子分类器,它将部分文档分为四类:根据事实检查的必要性。我们假设有包含错误信息的典型描述。因此,如果我们收集文档及其修订文档,并标记其修订是否为更正,我们可以通过学习数据集来构建分类器。为了构建这个分类器,我们建立了一个数据集,其中包括一组来自维基百科编辑历史的多次修改的句子。标签表示编辑对句子的修改程度。我们开发了一个基于web的系统来演示我们提出的方法。当我们输入文本时,系统会预测编辑应该重新确认文本的哪些部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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