App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 8

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 today. Entity omission in the training set provide 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 proposes a incorporating similarity negative sampling for distantly supervised named entity recognition. First, the similarity between the candidate samples and the labeled entity samples is calculated and scored; secondly, the candidate samples are sampled based on the similarity score, and the samples participating in the training are sampled. Compared with the random negative sampling method, this method reduces 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 , our 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 Accepted Paper, Vol. 41, 2024 No. 8

Publish History

[2024-02-05] Accepted Paper

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). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0577. (Liu Yang, Xian Yantuan, Xiang Yan, et al. Incorporating similarity negative sampling for distantly supervised ner [J]. Application Research of Computers, 2024, 41 (8). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.12.0577. )

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  • Application Research of Computers Monthly Journal
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    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.

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