A Named Entity Recognition Model Based on Rotational Attention

doi:10.15966/j.cnki.dnydx.2024.z1.011

Computer & Telecommunication

2024, Vol. 1

Issue (1): 21-25 DOI: 10.15966/j.cnki.dnydx.2024.z1.011

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A Named Entity Recognition Model Based on Rotational Attention

Guizhou Minzu University

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Abstract Addressing the issue of inadequate classification accuracy in named entity recognition via entity word-to-relationship modeling, we propose a method using rotational attention. Firstly, the text is encoded using the BERT model and Bi-LSTM, fol‐ lowed by extraction of features from input text using convolutional neural networks. Subsequently, the extracted feature sequence is inputted into the rotational attention model for output probability calculations, and the MLP layer is used for output classification. The study's outcomes affirm the efficacy and feasibility of the technique proposed in this paper, as it successfully yields superior re‐ sults on mainstream English databases including CADEC, GENIA and CoNLL2003, for named entity recognition exercise.

Key words： Named Entity Recognition Rotating Attention Word Pair Relationship classification

Published: 10 May 2024

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Cite this article:

LAI Xiao-meng　WEI Jia-yin. A Named Entity Recognition Model Based on Rotational Attention. Computer & Telecommunication, 2024, 1(1): 21-25.

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https://www.computertelecom.com.cn/EN/10.15966/j.cnki.dnydx.2024.z1.011 OR https://www.computertelecom.com.cn/EN/Y2024/V1/I1/21

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