Cohen and Hersh Briefings in Bioinformatics 2005

Citation

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

oxfordjournals

Summary

This is a survey paper about biomedical text mining in 2005.

They describe the state of the art in 2005 for each distinct type of text-mining task below.

• 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.

• 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

• 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

Related papers

The widely cited Pang et al EMNLP 2002 paper was influenced by this paper - but considers supervised learning techniques. The choice of movie reviews as the domain was suggested by the (relatively) poor performance of Turney's method on movies.

An interesting follow-up paper is Turney and Littman, TOIS 2003 which focuses on evaluation of the technique of using PMI for predicting the semantic orientation of words.