Abstract Named Entity Recognition (NER) is used to identify entity nouns in the corpus such
as Location, Person and Organization, etc. NER is also an important basic of research in …

The representational basis for downstream natural language processing tasks is word
embeddings, which capture lexical semantics in numerical form to handle the abstract …

With the rapid growth of Internet penetration, more and more people choose the Internet to
express their views on topics of interest. In recent years, named entity recognition (NER) is …

The goal of biomedical relation extraction is to obtain structured information from electronic
medical records by identifying relations among clinical entities. By integrating the …

The explosion of clinical textual data has drawn the attention of researchers. Owing to the
abundance of clinical data, it is becoming difficult for healthcare professionals to take real …

The use of BERT, one of the most popular language models, has led to improvements in
many Natural Language Processing (NLP) tasks. One such task is Named Entity …

Recurrent Neural Networks (RNNs) and transformers are deep learning models that have
achieved remarkable success in several Natural Language Processing (NLP) tasks since …

Abstract Medical Knowledge Graph (KG) has shown great potential in various healthcare
scenarios, such as drug recommendation and clinical decision support system. The factors …

Biomedical named entity recognition (BNER) is a critical task for biomedical information
extraction. Most popular BNER approaches based on deep learning utilize words and …

Recurrent neural network (RNN) has achieved remarkable success in sequence labeling
tasks with memory requirement. RNN can remember previous information of a sequence …