The position information of aspect is essential and useful for aspect-based sentiment analysis, while how to model the position of the aspect effectively during aspect-based sentiment analysis has not been well studied. Inspired by the intuition that the position prediction can help boost the performance of aspect-based sentiment analysis, we propose a D eep M ulti-T ask L earning (DMTL) model, which handles sentiment prediction (SP) and position prediction (PP) simultaneously. In particular, we first use a shared layer to learn the common features of the two tasks. Then, two task-specific layers are utilized to learn the features specific to the tasks and perform position prediction and sentiment prediction in parallel. Inspired by autoencoder structure, we design a position-aware attention and a deep bi-directional LSTM (DBi-LSTM) model for sentiment prediction and position prediction respectively to capture the position information better. Extensive experiments on four benchmark datasets show that our approach can effectively improve the performance of aspect-based sentiment analysis compared with the strong baselines.

方面的位置信息对于基于方面的情感分析至关重要且有用，而如何在基于方面的情感分析过程中有效地建模方面的位置尚未得到很好的研究。由直觉位置预测可以帮助提高基于方面，情感分析的性能的启发，我们提出了一个d EEP中号ulti-牛逼问大号收入（DMTL）模型，该模型可同时处理情绪预测（SP）和位置预测（PP）。特别是，我们首先使用共享层来学习这两个任务的共同特征。然后，利用两个特定于任务的层来学习特定于任务的特征，并并行执行位置预测和情感预测。受自动编码器结构的启发，我们分别设计了位置感知注意力和深度双向LSTM（DBi-LSTM）模型，分别用于情感预测和位置预测，以更好地捕获位置信息。在四个基准数据集上进行的大量实验表明，与强基准相比，我们的方法可以有效地提高基于方面的情感分析的性能。