Abstract
Target extraction is an important task in target-based sentiment analysis, which aims at identifying the boundary of target in given text. Previous works mainly utilize conditional random field (CRF) with a lot of handcraft features to recognize the target. However, it is hard to manually extract effective features to boost the performance of CRF-based methods. In this paper, we employ gated recurrent units (GRU) with label inference, to find valid label path for word sequence. At the same time, we find that character-level features play important roles in target extraction, and represent each word by concatenating word embedding and character-level representations which are learned via character-level GRU. Further, we capture boundary features of each word from its context words by convolution neural networks to assist the identification of the target boundary, since the boundary of a target is highly related to its context words. Experiments on two datasets show that our model outperforms CRF-based approaches and demonstrate the effectiveness of features learned from character-level and context words.
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Acknowledgments
We would like to thank the anonymous reviewers for their insightful suggestions. Our work is supported by National Natural Science Foundation of China (No. 61370117). The corresponding author of this paper is Houfeng Wang.
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Ma, D., Li, S., Wang, H. (2018). Target Extraction via Feature-Enriched Neural Networks Model. 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_30
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