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Applications of natural language processing in biodiversity science
Anne E Thessen1, Hong Cui, Dmitry Mozzherin
1Center for Library and Informatics, Marine Biological Laboratory, 7 MBL Street, Woods Hole, MA 02543, USA.
Advances in Bioinformatics
|June 12, 2012
Summary
Extracting biological information from scientific literature is crucial for data-driven biology. Natural language processing (NLP) and machine learning algorithms can automate this, but require specialized development for biological text.
Area of Science:
- Biodiversity Science
- Bioinformatics
- Computational Biology
Background:
- Vast biological knowledge is locked in scientific literature, exceeding human capacity for review.
- Transforming biology into a data-driven science necessitates large-scale information mining from this literature.
- Computers can process volume but struggle with the nuances of biological language.
Purpose of the Study:
- To review and discuss the application of natural language processing (NLP) and machine learning algorithms for extracting information from systematic biological literature.
- To highlight the challenges and progress in developing NLP tools for the biological domain.
- To outline key steps for applying information extraction tools to enhance biodiversity science.
Main Methods:
- Review of existing natural language processing (NLP) and machine learning algorithms.
- Discussion of specialized algorithm development for biological language.
- Examination of tools for biological information extraction (e.g., cellular processes, taxonomy, morphology).
Main Results:
- NLP algorithms require specific adaptation for biological terminology and syntax.
- Existing tools for biological information extraction are often domain-specific and need further validation.
- Progress has been made in automated taxonomic annotation and morphological character extraction.
Conclusions:
- Automated information extraction using NLP and machine learning is vital for advancing data-driven biology.
- Continued development and life-wide application of these tools are necessary for comprehensive biodiversity science.
- Specialized NLP approaches are key to unlocking the information within the biological literature.
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