TELKOMNIKA Telecommunication Computing Electronics and Control
Biomedical-named entity recognition using CUDA accelerated KNN algorithm

TELKOMNIKA Telecommunication Computing Electronics and Control
Biomedical-named entity recognition using CUDA accelerated KNN algorithm

BioNLP
Graphics processing unit
Machine learning
Named entity recognition
Natural language processing

Biomedical named entity recognition (Bio-NER) is a highly complex and
time-consuming research domain using natural language processing (NLP).
It’s widely used in information retrieval, knowledge summarization,
biomolecular event extraction, and discovery applications. This paper
proposes a method for the recognition and classification of named entities in
the biomedical domain using machine learning (ML) techniques. Support
vector machine (SVM), decision trees (DT), K-nearest neighbor (KNN), and
its kernel versions are used. However, recent advancements in
programmable, massively parallel graphics processing units (GPU) hold
promise in terms of increased computational capacity at a lower cost to
address multi-dimensional data and time complexity. We implement a novel
parallel version of KNN by porting the distance computation step on GPU
using the compute unified device architecture (CUDA) and compare the
performance of all the algorithms using the BioNLP/NLPBA 2004 corpus.
Results demonstrate that CUDA-KNN takes full advantage of the GPU’s
computational capacity and multi-leveled memory architecture, resulting in a
35× performance enhancement over the central processing unit (CPU). In a
comparative study with existing research, the proposed model provides an
option for a faster NER system for higher dimensionality and larger datasets
as it offers balanced performance in terms of accuracy and speed-up, thus
providing critical design insights into developing a robust BioNLP system.

Manish Bali, Anandaraj Shanthi Pichandi, Jude Hemanth Duraisamy

http://telkomnika.uad.ac.id

Feb 16, 2023

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