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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Volume 18 > No. 1

 
   

Towards Dynamic Verifiable Pattern Matching

PP: 42-46
doi:10.18576/amis/170525
Author(s)
N Giridharan, S Poojana, S Sujitha, P Mithun,
Abstract
The process of identifying examples from the field of biomedicine include chemicals, genes, proteins, viruses, illnesses, DNAs, and RNAs is known as "biomedical named entity identification" (BNER). The main challenge with BNER is figuring out how to get rid of these components. The majority of the BNER methodologies utilized supervised machine learning (SML) techniques. Features are crucial in SML approaches to boost the effectiveness of the recognition process. A feature is characterized as a collection of differentiating and distinguishing characteristics that may indicate the existence of an entity. It is a challenging study area that lays the groundwork for obtaining a significant amount of biomedical knowledge from unstructured texts and organizing it into organized representations. The framework for sequence labeling is being used for biological named entity recognition (BioNER). The outcomes of this approach, however, are not always favorable as it frequently fails to utilize the semantic data in the dataset effectively.

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