Version 1
: Received: 30 May 2024 / Approved: 30 May 2024 / Online: 30 May 2024 (13:41:05 CEST)
How to cite:
Peng, C.; Wang, X.; Li, Q.; Yu, Q.; Jiang, R.; Ma, W.; Wu, W.; Meng, R.; Li, H.; Huai, H.; Wang, S.; He, L. Named Entity Recognition Based on Contrastive Learning and Enhanced Lexicon for Pig Diseases of Chinese Corpus. Preprints2024, 2024052054. https://doi.org/10.20944/preprints202405.2054.v1
Peng, C.; Wang, X.; Li, Q.; Yu, Q.; Jiang, R.; Ma, W.; Wu, W.; Meng, R.; Li, H.; Huai, H.; Wang, S.; He, L. Named Entity Recognition Based on Contrastive Learning and Enhanced Lexicon for Pig Diseases of Chinese Corpus. Preprints 2024, 2024052054. https://doi.org/10.20944/preprints202405.2054.v1
Peng, C.; Wang, X.; Li, Q.; Yu, Q.; Jiang, R.; Ma, W.; Wu, W.; Meng, R.; Li, H.; Huai, H.; Wang, S.; He, L. Named Entity Recognition Based on Contrastive Learning and Enhanced Lexicon for Pig Diseases of Chinese Corpus. Preprints2024, 2024052054. https://doi.org/10.20944/preprints202405.2054.v1
APA Style
Peng, C., Wang, X., Li, Q., Yu, Q., Jiang, R., Ma, W., Wu, W., Meng, R., Li, H., Huai, H., Wang, S., & He, L. (2024). Named Entity Recognition Based on Contrastive Learning and Enhanced Lexicon for Pig Diseases of Chinese Corpus. Preprints. https://doi.org/10.20944/preprints202405.2054.v1
Chicago/Turabian Style
Peng, C., Shuyan Wang and Longjuan He. 2024 "Named Entity Recognition Based on Contrastive Learning and Enhanced Lexicon for Pig Diseases of Chinese Corpus" Preprints. https://doi.org/10.20944/preprints202405.2054.v1
Abstract
Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.
Keywords
Chinese Named Entity Recognition; Pig disease; Lexicon enhanced; Contrastive learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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