Abstract
Neural networks based term extraction methods regard term extraction task as sequence labeling task. They make better modeling of natural language and eliminate the dependence of traditional term extraction methods on artificial features. CNN-BiLSTM-CRF model is proposed in this paper to minimize the influence of different word segmentation results on term extraction. Experiment results show that CNN-BiLSTM-CRF has higher stability than the baseline model for different word segmentation results.
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Han, X., Xu, L., Qiao, F. (2018). CNN-BiLSTM-CRF Model for Term Extraction in Chinese Corpus. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_25
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DOI: https://doi.org/10.1007/978-3-030-02934-0_25
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