Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

Background Concept extraction, a subdomain of natural language processing (NLP) with a
focus on extracting concepts of interest, has been adopted to computationally extract clinical …

Large language models (LLMs) have shown impressive abilities for open-domain NLP
tasks. However, LLMs are sometimes too footloose for natural language understanding …

Natural language is ubiquitous in the workflow of medical imaging. Radiologists create and
consume free text in their daily work, some of which can be amenable to enhancements …

Background Consisting of dictated free-text documents such as discharge summaries,
medical narratives are widely used in medical natural language processing. Relationships …

Information Extraction (IE), one of the important tasks in text analysis and Natural Language
Processing (NLP), involves extracting meaningful pieces of knowledge from unstructured …

The microbial and molecular ecology research communities have made substantial
progress on develo** standards for annotating samples with environment metadata …

Traditional clinical named entity recognition methods fail to balance the effectiveness of
feature extraction of unstructured text and the complexity of neural network models. We …

Objective The study aims to present an active learning approach that automatically extracts
clinical concepts from unstructured data and classifies them into explicit categories such as …

Abstract Clinical Named Entity Recognition (CNER), the task of identifying the entity
boundaries in clinical texts, is essential for many applications. Previous methods usually …