使用现成论证挖掘预测Amazon评论的可信度
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
摘要：
互联网用户以前所未有的速度生成内容。因此，迫切需要建立能够在信息海洋中识别有用内容的智能系统。在本文中，我们的目标是预测亚马逊评论的有用性，并为此利用来自现成的论证挖掘系统的功能。我们认为，评论的有用性，事实上，是严格相关的争论的内容，而使用一个已经训练过的系统避免了昂贵的需要重新标记一个新的数据集。一个大型的公开语料库上获得的结果支持这一假设。
英文摘要：
Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
