Yi Language Named Entity Recognition Method Based on CR-BACC Model

doi:10.3969/j.issn.1006-2475.2025.05.004

Abstract

Abstract: This paper constructs and makes publicly available a named entity recognition dataset （YNNER） based on Yi language news text， collected the Liangshan Daily news dataset， and manually annotated the names of people， places and institutions. Considering the BiLSTM-Attention model and CNN model， global sequence and local spatial features can be extracted， and the restoration of diphthonic characters in the text can reduce the error of label recognition. This paper designs a Character Replacement BiLSTM Attention CNN conditional random field model （CR-BACC） based on character replacement. Experiments are conducted on the Chinese MSRA， People’s Daily and Yi YNNER datasets and compared with three representative algorithms. Experimental results show the effectiveness of this method in the Yi language named entity recognition task. This paper aims to promote the development of research in the field of Yi named entity recognition by providing datasets and models for the field to extend related research.

Key words: , Yi language natural language processing, named entity recognition, long short-term memory network, attention mechanism, convolutional neural network, conditional random field

CLC Number:

TP391

WANG Chengxian1, ZHAO Qing2. Yi Language Named Entity Recognition Method Based on CR-BACC Model[J]. Computer and Modernization, 2025, 0(05): 28-35.

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