MedTAG

MedTAG supports annotation of biomedical documents to generate richly annotated datasets for Named Entity Recognition and Linking (NER+L) using Semantic Annotators and Natural Language Processing (NLP) methods.

Key Features:

• Named Entity Recognition and Linking (NER+L) support: Facilitates annotation workflows targeted at NER+L tasks in the biomedical domain.
• Semantic Annotators integration: Enables the use of semantic annotators for concept identification and linking.
• Natural Language Processing (NLP) methods: Supports NLP-based annotation approaches for processing unstructured biomedical text.
• Collaborative annotation support: Allows multiple annotators to work on the same documents to produce shared annotations.
• Customizability: Provides configurable annotation schemas and options to match domain-specific requirements.
• Scalable manual annotation: Has been applied to manual annotation at scale, including the annotation of over seven thousand clinical histopathology reports.

Scientific Applications:

• Histopathology report annotation: Used for manual annotation of more than 7,000 clinical histopathology reports by physicians and experts.
• NER+L and semantic annotator development: Produces richly annotated biomedical datasets for training and evaluating NER+L systems, semantic annotators, and other NLP methods.

Methodology:

Employs Semantic Annotators and Natural Language Processing (NLP) methods to support Named Entity Recognition and Linking (NER+L).