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Named entity recognition using point prediction and active learning

Authors:

Koga Kobayashi,

Kei WakabayashiAuthors Info & Claims

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

Pages 287 - 293

https://doi.org/10.1145/3366030.3366072

Published: 22 February 2020 Publication History

Abstract

Named entity recognition (NER) research has been spreading into specialty domains. A specialty domain corpus is smaller than a general domain corpus. Moreover, annotating a specialty domain corpus is more expensive than annotating a general corpus. Therefore, in this paper, we introduce a model that uses point-wise prediction and active learning to achieve a high extraction performance even in a small annotation corpus. We demonstrate the effectiveness of our approach through a simulation of active learning.

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Cited By

Cunha Santos A. V. Silva Neto Jde Paulo Faleiros T(2023)Investigation of Deep Active Self-learning Algorithms Applied to Named Entity RecognitionIntelligent Systems10.1007/978-3-031-45392-2_31(470-484)Online publication date: 12-Oct-2023
https://doi.org/10.1007/978-3-031-45392-2_31
Saito RKobayashi KWakabayashi K(2021)Efficient Training Method for Phrase Extraction Models using Natural Language ExplanationsThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487703(288-295)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487703

Index Terms

Named entity recognition using point prediction and active learning
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Other conferences

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

December 2019

709 pages

ISBN:9781450371797

DOI:10.1145/3366030

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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JKU: Johannes Kepler Universität Linz
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2020

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iiWAS2019

iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2019

Munich, Germany

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Cited By

Cunha Santos A. V. Silva Neto Jde Paulo Faleiros T(2023)Investigation of Deep Active Self-learning Algorithms Applied to Named Entity RecognitionIntelligent Systems10.1007/978-3-031-45392-2_31(470-484)Online publication date: 12-Oct-2023
https://doi.org/10.1007/978-3-031-45392-2_31
Saito RKobayashi KWakabayashi K(2021)Efficient Training Method for Phrase Extraction Models using Natural Language ExplanationsThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487703(288-295)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487703

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