CNERVis: a visual diagnosis tool for Chinese named entity recognition

Pei Shan Lo, Jian Lin Wu, Syu Ting Deng, Ko Chih Wang*

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Abstract: Named entity recognition (NER) is a crucial initial task that identifies both spans and types of named entities to extract the specific information, such as organization, person, location, and time. Nowadays, the NER task achieves state-of-the-art performance by deep learning approaches for capturing contextual features. However, the complex structures of deep learning make a black-box problem and limit researchers’ ability to improve it. Unlike the Latin alphabet, Chinese (or other languages such as Korean and Japanese) do not have an explicit word boundary. Therefore, some preliminary works, such as word segmentation (WS) and part-of-speech tagging (POS), are needed before the Chinese NER task. The correctness of preliminary works importantly influences the final NER prediction. Thus, investigating the model behavior of the Chinese NER task becomes more complicated and challenging. In this paper, we present CNERVis, a visual analysis tool that allows users to interactively inspect the WS-POS-NER pipeline and understand how and why a NER prediction is made. Also, CNERVis allows users to load the numerous testing data and explores the critical instances to facilitate the analysis from large datasets. Our tool’s usability and effectiveness are demonstrated through case studies. Graphic abstract: [Figure not available: see fulltext.].

原文英語
頁(從 - 到)653-669
頁數17
期刊Journal of Visualization
25
發行號3
DOIs
出版狀態已發佈 - 2022 6月

ASJC Scopus subject areas

  • 凝聚態物理學
  • 電氣與電子工程

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