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Bi-directional long short term memory using recurrent neural network for biological entity recognition

Siddalingappa, R and Sekar, K (2022) Bi-directional long short term memory using recurrent neural network for biological entity recognition. In: IAES International Journal of Artificial Intelligence, 11 (1). pp. 89-101.

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Official URL: https://doi.org/10.11591/ijai.v11.i1.pp89-101


Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

Item Type: Journal Article
Publication: IAES International Journal of Artificial Intelligence
Publisher: Institute of Advanced Engineering and Science
Additional Information: The copyright for this article belongs to the Authors.
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 11 May 2022 17:33
Last Modified: 11 May 2022 17:33
URI: https://eprints.iisc.ac.in/id/eprint/71622

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