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Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

Authors:

Pratyay Banerjee,

Kuntal Kumar Pal,

Murthy Devarakonda,

Chitta BaralAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 2, Issue 4

Article No.: 33, Pages 1 - 24

https://doi.org/10.1145/3465221

Published: 18 July 2021 Publication History

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Abstract

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B, I, O only) compared to multiple entity-classes in traditional NER (i.e., B_entity1, B_entity2, I_entity1, I, O), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

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Cited By

Park YSon GRho M(2024)Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and AdvancesApplied Sciences10.3390/app1420930214:20(9302)Online publication date: 12-Oct-2024
https://doi.org/10.3390/app14209302
Bian JZheng JZhang YZhou HZhu S(2024)One-shot Biomedical Named Entity Recognition via Knowledge-Inspired Large Language ModelProceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3698587.3701356(1-10)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3698587.3701356
Ahmed AHuang DArafat SHameed I(2024)Enriching Urdu NER with BERT Embedding, Data Augmentation, and Hybrid Encoder-CNN ArchitectureACM Transactions on Asian and Low-Resource Language Information Processing10.1145/364836223:4(1-38)Online publication date: 15-Apr-2024
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Index Terms

Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 2, Issue 4

October 2021

199 pages

EISSN:2637-8051

DOI:10.1145/3476827

Editors:
Insup Lee
University of Pennsylvania, USA
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John A. Stankovic
University of Virginia, USA

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Publication History

Published: 18 July 2021

Accepted: 01 May 2021

Revised: 01 April 2021

Received: 01 September 2020

Published in HEALTH Volume 2, Issue 4

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Cited By

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Bian JZheng JZhang YZhou HZhu S(2024)One-shot Biomedical Named Entity Recognition via Knowledge-Inspired Large Language ModelProceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3698587.3701356(1-10)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3698587.3701356
Ahmed AHuang DArafat SHameed I(2024)Enriching Urdu NER with BERT Embedding, Data Augmentation, and Hybrid Encoder-CNN ArchitectureACM Transactions on Asian and Low-Resource Language Information Processing10.1145/364836223:4(1-38)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3648362
Barlybayev AMukanova A(2024)Advancements in Geospatial Question-Answering Systems: A Case Study on the Implementation in the Kazakh Language2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)10.1109/PIERE62470.2024.10805012(1710-1715)Online publication date: 15-Nov-2024
https://doi.org/10.1109/PIERE62470.2024.10805012
M AS KS JS J AK R B(2024)Bio Medical Named Entity Recognition Using Large Language Models2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP61711.2024.10870607(01-06)Online publication date: 26-Oct-2024
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Shishehgarkhaneh MMoehler RFang YHijazi AAboutorab H(2024)Transformer-Based Named Entity Recognition in Construction Supply Chain Risk Management in AustraliaIEEE Access10.1109/ACCESS.2024.337723212(41829-41851)Online publication date: 2024
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Chen PWang JLin HZhang YYang Z(2023)Knowledge Adaptive Multi-Way Matching Network for Biomedical Named Entity Recognition via Machine Reading ComprehensionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2022.323385620:3(2101-2111)Online publication date: 3-Jan-2023
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