Knowledge Enhanced Fine-grained Biomedical Named Entity Recognition
Pages 241 - 246
Abstract
While Named Entity Recognition (NER) in universal domains has made significant progress, there is still room for improvement in Biomedical Named Entity Recognition (BioNER) as it pertains to specific domains. Unlike the universal domain, NER in specialized domains requires a more detailed categorization of entities. Based on the idea by incorporating knowledge, we propose a method that is better suited for this task. Our method initially employs comparative learning to thoroughly grasp entity category information as additional knowledge. Subsequently, we seamlessly integrate this category information into the NER process. By enhancing knowledge through the incorporation of category information into entity extraction, our method improves the F1 score by 1% compared to existing methods.
References
[1]
[1] J. Li, A. Sun, J. Han and C. Li, A Survey on Deep Learning for Named Entity Recognition, in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 1, pp. 50-70, 1 Jan. 2022.
[2]
[2] Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 142–147.
[3]
[3] Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, and Kewei Tu. 2021. Automated Concatenation of Embeddings for Structured Prediction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2643–2660.
[4]
[4] ACE 2005 Multilingual Training Corpus, https://catalog.ldc.upenn.edu/LDC2006T06. Last accessed 15 February 2006
[5]
[5] J.-D. Kim, T. Ohta, Y. Tateisi, and J. Tsujii. GENIA corpus—a semantically annotated corpus for bio-textmining. Bioinformatics, Volume 19, July 2003, Pages i180–i182.
[6]
[6] Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, and Yueting Zhuang. 2022. Parallel Instance Query Network for Named Entity Recognition. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 947–961.
[7]
[7] Hang Yan, Yu Sun, Xiaonan Li, and Xipeng Qiu. An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition. arXiv e-prints, 2022.
[8]
[8] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised Contrastive Learning. arXiv e-prints, 2020.
[9]
[9] Jue Wang, Lidan Shou, Ke Chen, and Gang Chen. Pyramid: A Layer ed Model for Nested Named Entity Recognition. 2020. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5918-5928.
[10]
[10] Takashi Shibuya, and Eduard Hovy. Nested Named Entity Recognition via Second best Sequence Learning and Decoding. 2020. Transactions of the Association for Computational Linguistics, 8:605-620.
[11]
[11] Juntao Yu, Bernd Bohnet, and Massimo Poesio. Named Entity Recognition as Dependency Parsing. 2020. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6470-6476.
[12]
[12] Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, and Shanshan Jiang. Gazetteer enhanced atten tive neural networks for named entity recognition. 2019. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 6233-6238.
[13]
[13] Wei Lu and Dan Roth. Joint mention extraction and classification with mention hypergraphs. 2015. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 857-867.
[14]
[14] Zita Marinho, Alfonso Mendes, Sebastiao Miranda, and David Nogueira. Hierarchical nested named entity recognition. 2019. In Proceedings of the 2nd Clinical Natural Language Processing Workshop. 28-34.
[15]
[15] Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, and Fei Li. 2022. Unified Named Entity Recognition as Word-Word Relation Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 10965-10973.
[16]
[16] Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, and Yueting Zhuang. 2022. Parallel Instance Query Network for Named Entity Recognition. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 947–961.
[17]
[17] Gunel Beliz, Du Jingfei, Conneau Alexis,and Stoyanov Ves. Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning. arXiv e-prints, 2020.
Index Terms
- Knowledge Enhanced Fine-grained Biomedical Named Entity Recognition
Recommendations
Fine-grained Dutch named entity recognition
This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and ...
Unsupervised biomedical named entity recognition
Display Omitted BM-NER is approached by an unsupervised stepwise method.Noun phrase chunking is a good approximation of boundary detection.Distributional semantics works well in classifying entities.The system performs well on clinical and biological ...
Named entity recognition in Wikipedia
People's Web '09: Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic ResourcesNamed entity recognition (NER) is used in many domains beyond the newswire text that comprises current gold-standard corpora. Recent work has used Wikipedia's link structure to automatically generate near gold-standard annotations. Until now, these ...
Comments
Information & Contributors
Information
Published In
October 2023
589 pages
ISBN:9798400707988
DOI:10.1145/3633637
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 February 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- National Key R&D Program of China
Conference
ICCPR 2023
ICCPR 2023: 2023 12th International Conference on Computing and Pattern Recognition
October 27 - 29, 2023
Qingdao, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 18Total Downloads
- Downloads (Last 12 months)18
- Downloads (Last 6 weeks)5
Reflects downloads up to 13 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format