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Research on Named Entity Recognition in Inorganic Chemistry Based on Bert-BiLSTM-CRF

Published: 16 December 2022 Publication History

Abstract

Aiming at the problems of fuzzy entity boundary and poor recognition effect in named entity recognition in the field of inorganic chemistry, this paper adopts the BERT-BiLSTM-CRF model. First, the BERT pre-training model is used for semantic extraction to generate word vectors representing contextual semantic information, and then the word vectors are input into the BiLSTM model for training to obtain contextual features, and finally, the CRF model is used to obtain a reasonable sequence with the maximum probability. The entity recognition effect of the model used has the highest F1 value of 0.9429 on the test corpus. The experimental results show that the BERT-BiLSTM-CRF model can effectively identify named entities in the field of inorganic chemistry, and the recognition effect is better than other traditional models.

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  1. Research on Named Entity Recognition in Inorganic Chemistry Based on Bert-BiLSTM-CRF

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    ICBDT '22: Proceedings of the 5th International Conference on Big Data Technologies
    September 2022
    454 pages
    ISBN:9781450396875
    DOI:10.1145/3565291
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    Published: 16 December 2022

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    Author Tags

    1. BERT
    2. BiLSTM
    3. CRF
    4. Keyword: Named entity recognition

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