CONTEXTUAL LEARNING APPROACH FOR SYNDROME DISEASE NAMED ENTITY RECOGNITION
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Date
2023-03
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Publisher
Indian Society For Health And Advanced Research
Abstract
The groundwork for extracting a significant amount of biomedical information from unstructured texts into structured formats is the difficult research area of biological entity recognition from medical documents. The existing work implemented the named entity recognition for diseases using the sequence labelling framework. The performance of this strategy, however, is not always adequate, and it frequently cannot fully exploit the semantic information in the dataset. The Syndrome Diseases Named Entity problem is presented in this work as a sequence labelling with multi-context learning. By using well-designed text/queries, this formulation may incorporate more previous information and to decode it using decoding techniques such conditional random fields (CRF). We performed experiments on three biomedical datasets, and the outcomes show how effective our methodology is on the BC5CDR-Disease, JNLPBA and NCBI-Disease, compared with other techniques our methodology performs with accuracy levels of 96.70%,98.65 and 96.72% respectively.
Description
Keywords
sequence labelling, context learning, named entity recognition, Gated recurrent unit, Conditional random field
Citation
Uma Dr. E., K Kamatchi, Elangovan Mehala. CONTEXTUAL LEARNING APPROACH FOR SYNDROME DISEASE NAMED ENTITY RECOGNITION. Indian Journal Of Applied Research. 2023 Mar; 13(3): 11-14