Abstract
This paper presents a novel neural architecture for aspect term extraction in fine-grained sentiment computing area. In addition to amalgamating sequential features (character embedding, word embedding and POS tagging information), we train an end-to-end Recurrent Neural Networks (RNNs) with meticulously designed dependency transmission between recurrent units, thereby making it possible to learn structural syntactic phenomena. The experimental results show that incorporating these shallow semantic features improves aspect term extraction performance compared to a system that uses no linguistic information, demonstrating the utility of morphological information and syntactic structures for capturing the affinity between aspect words and their contexts.
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Acknowledgments
The research is supported by the National Natural Science Foundation of China (No. 61572145) and the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (No. 2017KZDXM031). We would like to acknowledge the anonymous reviewers for their helpful comments and suggestions.
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Guo, L., Jiang, S., Du, W., Gan, S. (2018). Recurrent Neural CRF for Aspect Term Extraction with Dependency Transmission. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_32
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