CNERVis: a visual diagnosis tool for Chinese named entity recognition

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.].

Original languageEnglish
Pages (from-to)653-669
Number of pages17
JournalJournal of Visualization
Issue number3
Publication statusPublished - 2022 Jun


  • BiLSTM
  • Chinese named entity recognition
  • natural language processing
  • sequence labeling
  • visual analytics

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electrical and Electronic Engineering


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