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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 8
2322-2328

Incorporating similarity negative sampling for distantly supervised NER

Liu Yang1,2
Xian Yantuan1,2
Xiang Yan1,2
Huang Yuxin1,2
1. Faculty of Information Engineering & Automation, Kunming University of Science & Technology, Kunming 650500, China
2. Yunnan Key Laboratory of Artificial Intelligence, Kunming 650500, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0577

Abstract

The entity omission is a typical problem of distantly supervised named entity recognition. Entity omission in the training set provides incorrect supervision information during model training, model will be more inclined to predict this type of entity as a non-entity when subsequently predicting entity types, resulting in a decline in the model's entity recognition and classification capabilities, and affects the generalization performance of the model. To deal with the problem, this paper proposed a incorporating similarity negative sampling for distantly supervised named entity recognition. Firstly, it calculated and scored the similarity between the candidate samples and the labeled entity samples. Secondly, it sampled the candidate samples based on the similarity score, and sampled the samples participating in the training. Compared with the random negative sampling method, this method reduced the possibility of sampling missing entities by combining similarity calculations, thereby improving the quality of training data and thus improving the performance of the model. Experimental results show that compared with other models on the three data sets of CoNLL03, Wiki, and Twitter, compared with the baseline model, the proposed model achieved an average F1 value improvement of about 5 percentage points. It is proved that this method can effectively alleviate the problem of performance degradation of the named entity recognition model caused by missing entities under distantly supervised conditions.

Foundation Support

国家自然科学基金资助项目(62266028)
云南重大科技专项计划课题(202202AD080003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0577
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 8
Section: Algorithm Research & Explore
Pages: 2322-2328
Serial Number: 1001-3695(2024)08-011-2322-07

Publish History

[2024-02-05] Accepted Paper
[2024-08-05] Printed Article

Cite This Article

刘杨, 线岩团, 相艳, 等. 融合相似度负采样的远程监督命名实体识别方法 [J]. 计算机应用研究, 2024, 41 (8): 2322-2328. (Liu Yang, Xian Yantuan, Xiang Yan, et al. Incorporating similarity negative sampling for distantly supervised NER [J]. Application Research of Computers, 2024, 41 (8): 2322-2328. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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