Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2886–2892 Copenhagen, Denmark, September 7–11, 2017. c⃝2017 Association for Computational Linguistics Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction∗ Xin Li and Wai Lam Key Laboratory on High Confidence Software Technologies (Sub-Lab, CUHK), Ministry of Education, and Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Hong Kong {lixin, wlam}@se.cuhk.edu.hk Abstract We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sen- tences. Two LSTMs equipped with ex- tended memories and neural memory op- erations are designed for jointly handling the extraction tasks of aspects and opin- ions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework. 1 Introduction The aspect-based sentiment analysis (ABSA) task is to identify opinions expressed towards specific entities such as laptop or attributes of entities such as price (Liu, 2012a). This task involves three sub- tasks: Aspect Term Extraction (ATE), Aspect Po- larity Detection and Aspect Category Detection. As a fundamental subtask in ABSA, the goal of the ATE task is to identify opinionated aspect ex- pressions. One of most important characteristics is that opinion words can provide indicative clues for aspect detection since opinion words should co-occur with aspect words. Most publicly avail- able datasets contain the gold standard annotations for opinionated aspects, but the ground truth of the corresponding opinion words is not commonly provided. Some works tackling the ATE task ig- nore the consideration of opinion words and just focus on aspect term modeling and learning (Jin ∗The work described in this paper is substantially sup- ported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14203414). We thank Lidong Bing and Piji Li for their helpful comments on this draft and the anonymous reviewers for their valuable feedback. et al., 2009; Jakob and Gurevych, 2010; Toh and Wang, 2014; Chernyshevich, 2014; Manek et al., 2017; San Vicente et al., 2015; Liu et al., 2015; Poria et al., 2016; Toh and Su, 2016; Yin et al., 2016). They fail to leverage opinion information which is supposed to be useful clues. Some works tackling the ATE task con- sider opinion information (Hu and Liu, 2004a,b; Popescu and Etzioni, 2005; Zhuang et al., 2006; Qiu et al., 2011; Liu et al., 2012b, 2013a,b, 2014) in an unsupervised or partially supervised manner. Qiu et al. (2011) proposed Double Propagation (DP) to collectively extract aspect terms and opin- ion words based on information propagation over a dependency graph. One drawback is that it heav- ily relies on the dependency parser, which is prone to generate mistakes when applying on informal online reviews. Liu et al. (2014) modeled relation between aspects and opinions by constructing a bi- partite heterogenous graph. It cannot perform well without a high-quality phrase chunker and POS tagger reducing its …

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