ATE_ABSITA @ EVALITA2020:方面术语提取和基于方面的情感分析任务概述

Lorenzo De Mattei, Graziella De Martino, Andrea Iovine, Alessio Miaschi, Marco Polignano, Giulia Rambelli
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引用次数: 9

摘要

在过去的几年里,新型情感分析技术的兴起,用于评估基于方面的产品评论意见,已成为为消费者和企业提供有价值见解的关键组成部分。为此,我们提出了ATE ABSITA: EVALITA 2020关于方面术语提取和基于方面的情感分析的共享任务。特别是,我们将任务作为三个子任务的级联处理:方面术语提取(ATE),基于方面的情感分析(ABSA)和情感分析(SA)。因此,我们邀请参与者提交旨在自动识别每个评论中的“方面术语”的系统,并预测每个方面表达的情感,以及整个评论的情感。这项任务引起了广泛的兴趣,共有27个团队注册,超过45名参与者。然而,只有三个团队提交了他们的工作系统。获得的结果强调了任务的难度,但它们也显示了如何使用创新的方法和模型来处理它。事实上,其中两个是基于大型预训练语言模型的,这是目前英语语言的典型技术。(de Mattei et al., 2020)“本文作者版权所有©2020。在知识共享许可国际署名4.0 (CC BY 4.0)下允许使用。
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ATE_ABSITA @ EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task
Over the last years, the rise of novel sentiment analysis techniques to assess aspect-based opinions on product reviews has become a key component for providing valuable insights to both consumers and businesses. To this extent, we propose ATE ABSITA: the EVALITA 2020 shared task on Aspect Term Extraction and Aspect-Based Sentiment Analysis. In particular, we approach the task as a cascade of three subtasks: Aspect Term Extraction (ATE), Aspect-based Sentiment Analysis (ABSA) and Sentiment Analysis (SA). Therefore, we invited participants to submit systems designed to automatically identify the ”aspect term” in each review and to predict the sentiment expressed for each aspect, along with the sentiment of the entire review. The task received broad interest, with 27 teams registered and more than 45 participants. However, only three teams submitted their working systems. The results obtained underline the task’s difficulty, but they also show how it is possible to deal with it using innovative approaches and models. Indeed, two of them are based on large pre-trained language models as typical in the current state of the art for the English language. (de Mattei et al., 2020) “Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).”
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