Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

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

Motivation Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language processing …

Objective Medical knowledge graph (KG) is attracting attention from both academic and
healthcare industry due to its power in intelligent healthcare applications. In this paper, we …

Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …

As the generation and accumulation of massive electronic health records (EHR), how to
effectively extract the valuable medical information from EHR has been a popular research …

The word embedding of traditional named entity recognition (NER) methods can't represent
the polysemy of a word, can't fully consider contextual information, and the local features in …

Recognition of biomedical entities from literature is a challenging research focus, which is
the foundation for extracting a large amount of biomedical knowledge existing in …

Background In biomedical text mining, named entity recognition (NER) is an important task
used to extract information from biomedical articles. Previously proposed methods for NER …

With the rapid advancement of technology and the necessity of processing large amounts of
data, biomedical Named Entity Recognition (NER) has become an essential technique for …