Fused Sequential and Hierarchical Representation Model for Aspect Term Extraction

In aspect-based sentiment analysis, a fundamental task is extracting aspect terms from opinionated sentences. While deep learning-based methods have achieved great progress in aspect term extraction (ATE), they mainly consider sequential semantic information and generally ignore the utilization of syntactic relations of the whole sentence on overall meanings. Furthermore, performances of these methods may also be diminished by poor handling of relation and text noises. To address these issues, we propose a Fused Sequential and Hierarchical Representation (FSHR) model, wherein both sequential and hierarchical representations are generated, which facilitates not only the capture of linear semantic information for predicting meaning-related aspect terms, but also the utilization of syntactic relations over the entire sentence to better identify structure-related aspect terms. Moreover, to refine the aspect representation, we incorporate relation gate mechanism which selectively activates meaningful syntactic dependency paths and design the multi-way aspect attention which prompts the model to focus on relevant text segments about particular aspects. Eventually, sequential and hierarchical representations are adaptively fused for aspect prediction. Experiment results on two datasets demonstrate FSHR outperforms competitive baselines and further extensive analyses reveal the effectiveness of our model.