In this study, named entity recognition is constructed and applied in the medical domain.
Data is labeled in BIO format. For example,“muscle” would be labeled “B-BODY” and “I …

1 Introduction Page 1 Journal of Modern Technology and Engineering Vol.6, No.2, 2021, pp.104-111
COMPARISON OF SPACY AND STANFORD LIBRARIES’ PRE-TRAINED DEEP LEARNING …

The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …

Biomedical named entity recognition (BioNER) is a preliminary task for many other tasks, eg,
relation extraction and semantic search. Extracting the text of interest from biomedical …

Named entity recognition (NER) is a subfield of information extraction, which aims to detect
and classify predefined named entities (eg, people, locations, organizations, etc.) in a body …

Biomedical named entity recognition (NER) is a critial task that aims to identify structured
information in clinical text, which is often replete with complex, technical terms and a high …

Named Entity Recognition (NER) has been an active research fields in biomedical text
mining. In the past years, much attention has been focused on semantic types related to …

Identifying named entities from unstructured biomedical text is an important part of
information extraction. The irrelevant words in long biomedical sentences and the complex …

Biomedical named entity recognition (BNER), which extracts important named entities such
as genes and proteins, is a challenging task in automated systems that mine knowledge in …

Abstract Background Electronic Medical Record (EMR) comprises patients' medical
information gathered by medical stuff for providing better health care. Named Entity …