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A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora.

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Title: A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora.
Authors: Yan, Chengxi, Tang, Xuemei, Yang, Hao, Wang, Jun
Source: Aslib Journal of Information Management; 2023, Vol. 75 Issue 3, p455-480, 26p
Subject Terms: DEEP learning, DATA mining, CORPORA, DATA augmentation, ACTIVE learning, QUALITY control
Abstract: Purpose: The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction. Design/methodology/approach: The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation ("SA-ALC"), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system. Findings: Extensive experiments on two types of Chinese classical historical datasets show that the authors' solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors' pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage. Originality/value: The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways. [ABSTRACT FROM AUTHOR]
Copyright of Aslib Journal of Information Management is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yan%2C+Chengxi%22">Yan, Chengxi</searchLink><br /><searchLink fieldCode="AR" term="%22Tang%2C+Xuemei%22">Tang, Xuemei</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Hao%22">Yang, Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jun%22">Wang, Jun</searchLink>
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  Data: Aslib Journal of Information Management; 2023, Vol. 75 Issue 3, p455-480, 26p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+mining%22">DATA mining</searchLink><br /><searchLink fieldCode="DE" term="%22CORPORA%22">CORPORA</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+augmentation%22">DATA augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22ACTIVE+learning%22">ACTIVE learning</searchLink><br /><searchLink fieldCode="DE" term="%22QUALITY+control%22">QUALITY control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction. Design/methodology/approach: The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation ("SA-ALC"), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system. Findings: Extensive experiments on two types of Chinese classical historical datasets show that the authors' solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors' pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage. Originality/value: The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Aslib Journal of Information Management is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1108/AJIM-03-2022-0107
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      – Code: eng
        Text: English
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        PageCount: 26
        StartPage: 455
    Subjects:
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: DATA mining
        Type: general
      – SubjectFull: CORPORA
        Type: general
      – SubjectFull: DATA augmentation
        Type: general
      – SubjectFull: ACTIVE learning
        Type: general
      – SubjectFull: QUALITY control
        Type: general
    Titles:
      – TitleFull: A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora.
        Type: main
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          Name:
            NameFull: Yan, Chengxi
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            NameFull: Tang, Xuemei
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          Name:
            NameFull: Yang, Hao
      – PersonEntity:
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            NameFull: Wang, Jun
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          Dates:
            – D: 01
              M: 05
              Text: 2023
              Type: published
              Y: 2023
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            – TitleFull: Aslib Journal of Information Management
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