Enhancing named entity recognition with a novel BERT‐BiLSTM‐CRF‐RC joint training model for biomedical materials database

Enhancing named entity recognition with a novel BERT‐BiLSTM‐CRF‐RC joint training model for biomedical materials database

Abstract In this study, we propose a novel joint training model for named entity recognition (NER) that combines BERT, BiLSTM, CRF, and a reading comprehension (RC) mechanism. Traditional BERT‐BiLSTM‐CRF models often struggle with inaccurate boundary detection and excessive fragmentation of named en...

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Bibliographic Details
Main Authors: Mufei Li, Yan Zhuang, Ke Chen, Lin Han, Xiangfeng Li, Yongtao wei, Xiangdong Zhu, Mingli Yang, Guangfu Yin, Jiangli Lin, Xingdong Zhang
Format: Article
Language:English
Published: Wiley-VCH 2025-03-01
Series:Materials Genome Engineering Advances
Subjects:
automated material database
BERT‐BiLSTM‐CRF‐RC
named entity recognition
reading comprehension
Online Access:https://doi.org/10.1002/mgea.70001
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https://doi.org/10.1002/mgea.70001

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