TY  - EJOU
AU  - Cui, Xiaohui 
AU  - Song, Chao 
AU  - Li, Dongmei 
AU  - Qu, Xiaolong 
AU  - Long, Jiao 
AU  - Yang, Yu 
AU  - Zhang, Hanchao 

TI  - RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 78
IS  - 3
SN  - 1546-2226

AB  - Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on <b>RoB</b>ERTa and <b>G</b>lobal <b>P</b>ointer (RoBGP). Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors. It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information, effectively addressing the issue of long-distance dependencies. Furthermore, the Global Pointer model is employed to comprehensively recognize all nested entities in the text. We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models. This research confirms the effectiveness of RoBGP in Chinese biomedical NER, providing reliable technical support for biomedical information extraction and knowledge base construction.
KW  - Biomedicine; knowledge base; named entity recognition; pretrained language model; global pointer

DO  - 10.32604/cmc.2024.047321