Distantly Supervised Named Entity Recognition Combined with Prototypical Networks

被引：0
|
作者：
Luo S. [1 ]
Lin Z. [1 ]
Pan L. [1 ]
Wu Z. [1 ]
机构：
[1] School of Information and Electronics, Beijing Institute of Technology, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2023年 / 43卷 / 04期
关键词：
automatic corpus annotation; distant supervision; named entity recognition; positive-unlabeled learning{PUL); prototypical network;
D O I：
10.15918/j.tbit1001-0645.2022.098
中图分类号：
学科分类号：
摘要：
Aiming at the problem of entity category labeling errors in the process of using distant supervision to label text entities, it is difficult for the model to effectively distinguish the category characteristics of each entity and affect the accuracy of the model. A named entity recognition （NER）method was proposed in this paper. It was designed to use pre-trained prototypical network coding to correctly label entities to generate category prototype representations, and to filter those far away samples from category prototypes in the corpus. Experiments show that the use of the prototype network can effectively improve the annotation quality of the corpus and improve the performance of the model. © 2023 Beijing Institute of Technology. All rights reserved.
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页码：410 / 416
页数：6
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