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

Autor:	Mensah, Samuel, Sun, Kai, Aletras, Nikolaos
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence I.2.7
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at EMNLP 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.01238 Zobrazit plný text záznamu View this record from Arxiv