Anatomical entity mention recognition at literature scale.
Sampo Pyysalo1, Sophia Ananiadou
1National Centre for Text Mining and University of Manchester, Manchester, UK.
Bioinformatics (Oxford, England)
|October 29, 2013
Summary
AnatomyTagger is a new machine learning system that automatically recognizes anatomical entities in scientific text. This tool enhances understanding of biomedical literature by tagging anatomical mentions.
Area of Science:
- Biomedical Informatics
- Natural Language Processing
- Computational Biology
Background:
- Anatomical entities are crucial for understanding biomedical science.
- Automated recognition of anatomical mentions in text is underdeveloped.
- Existing methods for analyzing biomedical text lack focus on anatomical entities.
Purpose of the Study:
- To develop and present AnatomyTagger, a machine learning system for recognizing anatomical entity mentions in free-form text.
- To improve the automatic analysis of biomedical literature by accurately identifying anatomical terms.
Main Methods:
- Developed a machine learning-based system named AnatomyTagger.
- Integrated lexical resources from Unified Medical Language System (UMLS) and Open Biomedical Ontologies (OBO).
- Utilized word representations from unlabeled text, statistical truecasing, and non-local features.
- Trained and evaluated the system on an extensive, newly introduced corpus.
Main Results:
- AnatomyTagger achieves accurate recognition of anatomical entity mentions.
- The system was applied to automatically annotate the entire open access scientific literature.
- The annotations enhance services for the Europe PubMed Central literature database.
Conclusions:
- AnatomyTagger represents a significant advancement in automated anatomical entity recognition.
- The system's application to a large corpus provides valuable data for biomedical research.
- This work facilitates better understanding and utilization of biomedical scientific literature.
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