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Transformer and Multi-scale Convolution for Target-Oriented Sentiment Analysis

  • Yinxu PanEmail author
  • Binheng Song
  • Ningqi Luo
  • Xiaojun Chen
  • Hengbin Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)

Abstract

Target-oriented sentiment analysis aims to extract the sentiment polarity of a specific target in a sentence. In this paper, we propose a model based on transformers and multi-scale convolutions. The transformer which is based solely on attention mechanisms generalizes well in many natural language processing tasks. Convolution layers with multiple filters can efficiently extract n-gram features at many granularities on each receptive field. We conduct extensive experiments on three datasets: SemEval ABSA challenge Restaurant and Laptop dataset, Twitter dataset. Our framework achieves state-of-the-art results, including improving the accuracy of Restaurant dataset to 84.20% (5.81% absolute improvement), improving the accuracy of the Laptop dataset to 78.21% (4.23% absolute improvement), and improving the accuracy of the Twitter dataset to 72.98% (0.87% absolute improvement).

Keywords

Target-oriented sentiment analysis Multi-scale convolution Transformer 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yinxu Pan
    • 1
    • 2
    • 3
    Email author
  • Binheng Song
    • 1
    • 2
  • Ningqi Luo
    • 1
    • 2
  • Xiaojun Chen
    • 3
  • Hengbin Cui
    • 3
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Graduate School at ShenZhenTsinghua UniversityBeijingChina
  3. 3.Ant Financial Services GroupXihuChina

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