Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …

Objective Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …

Existing general clinical natural language processing (NLP) systems such as MetaMap and
Clinical Text Analysis and Knowledge Extraction System have been successfully applied to …

Objective The goal of this study is to explore transformer-based models (eg, Bidirectional
Encoder Representations from Transformers [BERT]) for clinical concept extraction and …

Develo** high-performance entity normalization algorithms that can alleviate the term
variation problem is of great interest to the biomedical community. Although deep learning …

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP)
task to extract important concepts (named entities) from clinical narratives. Researchers …

This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and
presents the evaluation results. It focused on two subtasks:(i) identification (Task A) and (ii) …

Background Most state-of-the-art biomedical entity normalization systems, such as rule-
based systems, merely rely on morphological information of entity mentions, but rarely …

Clinical Named Entity Recognition (NER) is a critical task for extracting important patient
information from clinical text to support clinical and translational research. This study …

Text mining of scientific libraries and social media has already proven itself as a reliable tool
for drug repurposing and hypothesis generation. The task of map** a disease mention to …