Background Finding biomedical named entities is one of the most essential tasks in
biomedical text mining. Recently, deep learning-based approaches have been applied to …

Abstract Named Entity Recognition (NER) is an information extraction technique for the
automatic recognition of named entities and their classification in a natural language text …

It is well known that the rapid growth and dissemination of the Internet has resulted in huge
amounts of information generated and shared, available in the form of textual data, images …

The existing biomedical named entity recognition methods do not use the syntactic
information in the corpus, resulting in low precision. To solve this problem, a biomedical …

In named entity recognition (NER) for biomedical literature, approaches based on combined
classifiers have demonstrated great performance improvement compared to a single (best) …

Biomedical named entity recognition (Bio-NER) is a fundamental step in biomedical text
mining. This paper presents a two-phase Bio-NER model targeting at JNLPBA task. Our two …

Background Biomedical named entity recognition (BioNER) is an important task for
understanding biomedical texts, which can be challenging due to the lack of large-scale …

In the biomedical field, huge amounts of data have been produced day by day. These data
drives the development of the biomedical area researches in so many ways. This paper …

The article discusses the possibilities of solving NER (Named Entity Recognition) problem
for medical texts in Arabic with limited availability of labeled datasets, as well as …

The typical pretrained model's feature extraction capabilities are insufficient for medical
named entity identification, and it is challenging to express word polysemy, resulting in a low …