LaDer: A Two-Stage Unsupervised Method for Stem Cell Entity Recognition Based on Reinforcement Learning-Reference-Cited by-同舟云学术

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LaDer: A Two-Stage Unsupervised Method for Stem Cell Entity Recognition Based on Reinforcement Learning

Published:2024-08-17 Issue: Volume: Page:
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Zhou Xueyang,Fu Qiming^ORCID,Chen Yanming,Wang Yunzhe,Lu You,Xia Youbing,Chen Jianping

Funder

National Key R &D Program of China

National Natural Science Foundation of China

University Natural Science Foundation of Jiangsu Province

Primary Research and Development Plan of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13369-024-09409-4.pdf

Reference30 articles.

1. Lajtha, L.G.: Stem cell concepts. Differentiation 14(1–3), 23–33 (1979)

2. Ali, M.N.; Tan, G.: Bidirectional encoder-decoder model for arabic named entity recognition. Arab. J. Sci. Eng. 44, 9693–9701 (2019)

3. Jimmy, L.; Nongmeikappam, K.; Naskar, S.K.: Bilstm-crf manipuri ner with character-level word representation. Arab. J. Sci. Eng. 48(2), 1715–1734 (2023)

4. Hamed, B.A.; Ibrahim, O.A.S.; Abd El-Hafeez, T.: A survey on improving pattern matching algorithms for biological sequences. Concurr. Comput.: Pract. Expe. 34(26), 7292 (2022)

5. Shang, J.; Liu, L.; Ren, X.; Gu, X.; Ren, T.; Han, J.: Learning named entity tagger using domain-specific dictionary. Preprint at https://doi.org/10.48550/arXiv.1809.03599 (2018)

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