Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP)
applications. Traditional NER models are effective but limited to a set of predefined entity …

A considerable effort has been made to extract biological and chemical entities, as well as
their relationships, from the scientific literature, either manually through traditional literature …

Biomedical literature has become a rich source of information for various applications.
Automatic text mining methods can make the processing of extracting information from a …

In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER),
a new method for improving Named Entity Recognition (NER) tasks using data …

Occupational data mining and analysis is an important task in understanding today's
industry and job market. Various machine learning techniques are proposed and gradually …

Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing
in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have …

Background Consumers increasingly use online resources for their health information
needs. While current search engines can address these needs to some extent, they …

Abstract Background Chemical and biomedical Named Entity Recognition (NER) is an
essential prerequisite task before effective text mining can begin for biochemical-text data …

The sparsity of labelled data is an obstacle to the development of Relation Extraction (RE)
models and the completion of databases in various biomedical areas. While being of high …

The automatic recognition of gene names and their corresponding database identifiers in
biomedical text is an important first step for many downstream text-mining applications …