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An Effective Biomedical Named Entity Recognition by Handling Imbalanced Data Sets Using Deep Learning and Rule-Based Methods

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Abstract

Biomedical named entity recognition (Bio-NER) is an important and fundamental task for biomedical text mining. Bio-NER has various applications, especially in Natural Language Processing, and its performance heavily impacts dependent tasks. However, the class imbalance problem is the major issue for an effective Bio-NER. Over the past few years, rule-based methods and deep learning-based methods have been widely used. However, they are not able to effectively handle class imbalance issues. Therefore, we proposed a hybrid method by taking advantage of both methods for Bio-NER, which is named Hybrid Bio-NER (HBio-NER). Furthermore, we propose a training data format called ‘single_entity-O’, which reduces the class imbalance problem. Data preprocessing and ‘single_entity-O’ data representation format improve the class balance, further improving the HBio-NER system performance. HBio-NER identifies the named entities using the Bidirectional Long Short-Term Memory (Bi-LSTM) model and named entity boundaries (begin, inside of entity mentions) using rules. The effectiveness of the proposed method is tested on two data sets (NCBI disease and CHEMDNER chemical data sets) with different word representation models (GloVe, Word2Vec and FastText). The comparative analysis indicates that significant performance gain is achieved by the proposed method especially for disease named entity recognition.

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Data Availability

The data that support the findings of this study are publicly available.

Notes

  1. https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/.

  2. Corpus. https://biocreative.bioinformatics.udel.edu/tasks/biocreative-iv/chemdner/.

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Authors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology, Calicut, Kerala, India

    S. M. Archana & Jay Prakash

  2. Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Pramod Kumar Singh

  3. Electronics and Communication Engineering, National Institute of Technology, Calicut, Kerala, India

    Waquar Ahmed

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  2. Jay Prakash
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Correspondence to S. M. Archana.

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Archana, S.M., Prakash, J., Singh, P.K. et al. An Effective Biomedical Named Entity Recognition by Handling Imbalanced Data Sets Using Deep Learning and Rule-Based Methods. SN COMPUT. SCI. 4, 650 (2023). https://doi.org/10.1007/s42979-023-02068-6

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