Abstract
Based on Baidu encyclopedia and CNKI botanical classification standards, we classify botanical terms into five categories in this paper: plant morphology and classification, plant ecology and geography, phytochemistry, plant heredity, plant technology and methods. In view of the difficulties in terms identification, we use a deep learning entity recognition model based on Bert-BLSTM-CRF to identify terms from large-scale corpus. In this paper, the Bert-BLSTM-CRF model is used to identify the professional terms in the field of botany, with an accuracy rate of 89.58%, a recall rate of 87.92% and an F1 value of 89.25%, indicating that the model could be effectively applied to the task of identifying the professional terms in botany. Based on the existing corpus, a comparative experiment is carried out in this paper, and the experimental results show that the model improves the recognition effect of professional terms in the field of botany.
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Wulamu, A., Chen, N., Yang, L., Wang, L., Shi, J. (2020). Identification of Botany Terminology Based on Bert-BLSTM-CRF. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_41
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DOI: https://doi.org/10.1007/978-3-030-57881-7_41
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