An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction

• URL: http://arxiv.org/abs/2109.01238v1
• Date: Thu, 2 Sep 2021 22:49:45 GMT
• Title: An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction
• Authors: Samuel Mensah, Kai Sun, Nikolaos Aletras
• Abstract summary: Target-oriented opinion words extraction (TOWE) is a new subtask of target-oriented sentiment analysis. We show that BiLSTM-based models can effectively encode position information into word representations. We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information.
• Score: 13.765146062545048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Target-oriented opinion words extraction (TOWE) (Fan et al., 2019b) is a new subtask of target-oriented sentiment analysis that aims to extract opinion words for a given aspect in text. Current state-of-the-art methods leverage position embeddings to capture the relative position of a word to the target. However, the performance of these methods depends on the ability to incorporate this information into word representations. In this paper, we explore a variety of text encoders based on pretrained word embeddings or language models that leverage part-of-speech and position embeddings, aiming to examine the actual contribution of each component in TOWE. We also adapt a graph convolutional network (GCN) to enhance word representations by incorporating syntactic information. Our experimental results demonstrate that BiLSTM-based models can effectively encode position information into word representations while using a GCN only achieves marginal gains. Interestingly, our simple methods outperform several state-of-the-art complex neural structures.

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