Abstract
Aspect Term Extraction (ATE) plays an important role in aspect-based sentiment analysis. Syntax-based neural models that learn rich linguistic knowledge have proven their effectiveness on ATE. However, previous approaches mainly focus on modeling syntactic structure, neglecting rich interactions along dependency arcs. Besides, these methods highly rely on results of dependency parsing and are sensitive to parsing noise. In this work, we introduce a syntax-directed attention network and a contextual gating mechanism to tackle these issues. Specifically, a graphical neural network is utilized to model interactions along dependency arcs. With the help of syntax-directed self-attention, it could directly operate on syntactic graph and obtain structural information. We further introduce a gating mechanism to synthesize syntactic information with structure-free features. This gate is utilized to reduce the effects of parsing noise. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmark datasets.
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References
Chen, K., Wang, R., Utiyama, M., Sumita, E., Zhao1, T.: Syntax-directed attention for neural machine translation. In: AAAI, pp. 4792–4799 (2018)
Chen, Z., Mukherjee, A., Liu, B.: Aspect extraction with automated prior knowledge learning. In: ACL, pp. 347–358 (2014)
Chernyshevich, M.: IHS R&D belarus: cross-domain extraction of product features using CRF. In: SemEval@COLING, pp. 309–313 (2014)
Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: HLT/EMNLP, pp. 355–362 (2005)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: AAAI, pp. 775–760 (2004)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. CoRR abs/1508.01991 (2015)
Irsoy, O., Cardie, C.: Opinion mining with deep recurrent neural networks. In: EMNLP, pp. 720–728 (2014)
Li, F., et al.: Structure-aware review mining and summarization. In: COLING, pp. 653–661 (2010)
Li, X., Lam, W.: Deep multi-task learning for aspect term extraction with memory interaction. In: EMNLP, pp. 2886–2892 (2017)
Luo, H., Li, T., Liu, B., Wang, B., Unger, H.: Improving aspect term extraction with bidirectional dependency tree representation. CoRR abs/1805.07889 (2018)
Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNS-CRF. CoRR abs/1603.01354 (2016)
Marcheggiani, D., Titov, I.: Encoding sentences with graph convolutional networks for semantic role labeling. In: EMNLP, pp. 1506–1515 (2017)
Toh, Z., Wang, W.: DLIREC: aspect term extraction and term polarity classification system. In: SemEval@COLING, pp. 235–240 (2014)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)
Vicente, I.S., Saralegi, X., Agerri, R.: EliXa: a modular and flexible ABSA platform. In: SemEval@NAACL-HLT, pp. 748–752 (2015)
Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Recursive neural conditional random fields for aspect-based sentiment analysis. In: EMNLP, pp. 616–626 (2016)
Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: AAAI, pp. 3316–3322 (2017)
Ye, H., Yan, Z., Luo, Z., Chao, W.: Dependency-tree based convolutional neural networks for aspect term extraction. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10235, pp. 350–362. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57529-2_28
Yin, Y., Wei, F., Dong, L., Xu, K., Zhang, M., Zhou, M.: Unsupervised word and dependency path embeddings for aspect term extraction. In: IJCAI, pp. 2979–2985 (2016)
Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. CoRR abs/1809.10185 (2018)
Zhuang, L., Jing, F., Zhu, X.: Movie review mining and summarization. In: CIKM, pp. 43–50 (2006)
Acknowledgements
We thank Xiao Liang, Hongzhi Zhang, Yunyan Zhang, Wenkai Zhang and Hongfeng Yu, and the anonymous reviewers for their thoughtful comments and suggestions.
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Zhang, J., Xu, G., Wang, X., Sun, X., Huang, T. (2019). Syntax-Aware Representation for Aspect Term Extraction. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11439. Springer, Cham. https://doi.org/10.1007/978-3-030-16148-4_10
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