An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction
Samuel Agyei Mensah, Kai Sun, Νικόλαος Αλέτρας · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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.