Cohen and Hersh Briefings in Bioinformatics 2005
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Aaron M. Cohen and William R. Hersh. 2005. A Survey of Current Work in Biomedical Text Mining. Briefings in Bioinformatics. Vol 6. No 1. 57-71.
Online version
Summary
This is a survey paper about biomedical text mining in 2005.
- Named entity recognition
- Problems
- No complete dictionary for most types of biological named entities
- ambiguous words and phrases
- multi names
- approaches are mainly categorized into three below
- lexicon based
- rule based
- statistically based
- performance
- overall, the performance of gene and protein NER systems is F-scores between 75 and 85 percent.
- Problems
- Text classification
- Synonym and abbreviation extraction
- Synonym
- use dictionary
- automatic extraction of gene name synonyms from biomedical free text
- SVM classifier-based
- pattern-based
- abbreviation
- either the full form or the abbreviation is often enclosed in parentheses.
- a variety of alignment and scoring methods
- Synonym
- Relationship extraction
- detect occurrences of a prespecified type of relationship between a pair of entities of given types
- manually generated template-based methods
- automatic template methods
- statistical methods
- NLP-based methods
mostly are about the relationships between genes and proteins
- Hypothesis generation
- uncover relationships that are not present in the text but instead are inferred by the presence of other more explicit relationships. uncover previously unrecognized relationships
- Integration frameworks
- integrated text-mining frameworks
- still in the research and development phrase.
- The authors' suggestions
- Access to full text is required
- Additional analytical methods with possible features are required for a particular application
- Researchers should consider actual users' needs. The performance of a system with certain metrics does not guarantee users' satisfaction.
- Shared challenge tasks should be continued
