Named entity recognition (NER) aims to identify the required entities and their types from
unstructured text, which can be utilized for the construction of knowledge graphs. Traditional …

Abstract Machine learning (ML) has achieved substantial success in performing healthcare
tasks in which the configuration of every part of the ML pipeline relies heavily on technical …

In the era of information explosion, named entity recognition (NER) has attracted
widespread attention in the field of natural language processing, as it is fundamental to …

Clinical and biomedical concept extraction is critical in medical analysis using clinical and
biomedical documents from professional literature, EHRs and PHRs. Named entity …

Scientific contribution is typically embodiment of the value of a scientific publication, which
reflects the inspiration, promotion, and improvement of the publication on existing theories or …

Abstract Information extraction (IE) aims to discover and extract valuable information from
unstructured text. This problem can be decomposed into two subtasks: named entity …

The vigorous development of patent applications in recent years provides an opportunity to
unveil the inherent laws of innovation, but it also puts forward higher requirements for patent …

Pre-trained word embedding has a significant impact on constructing representations for
sentences, paragraphs and documents. However, existing word embedding methods are …

Entity recognition tasks, which aim to utilize the deep learning-based models to identify the
agricultural diseases and pests-related nouns such as the names of diseases, pests, and …

One of the primary tasks in the early stages of data mining involves the identification of
entities from biomedical corpora. Traditional approaches relying on robust feature …