A multi-strategy approach to biological named entity recognition

John Atkinson, Veronica Bull

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Recognizing and disambiguating bio-entities (genes, proteins, cells, etc.) names are very challenging tasks as some biologica databases can be outdated, names may not be normalized, abbreviations are used, syntactic and word order is modified, etc. Thus, the same bio-entity might be written into different ways making searching tasks a key obstacle as many candidate relevant literature containing those entities might not be found. As consequence, the same protein mention but using different names should be looked for or the same discovered protein name is being used to name a new protein using completely different features hence named-entity recognition methods are required. In this paper, we developed a bio-entity recognition model which combines different classification methods and incorporates simple pre-processing tasks for bio-entities (genes and proteins) recognition is presented. Linguistic pre-processing and feature representation for training and testing is observed to positively affect the overall performance of the method, showing promising results. Unlike some state-of-the-art methods, the approach does not require additional knowledge bases or specific-purpose tasks for post processing which make it more appealing. Experiments showing the promise of the model compared to other state-of-the-art methods are discussed.

Original languageEnglish
Pages (from-to)12968-12974
Number of pages7
JournalExpert Systems with Applications
Volume39
Issue number17
DOIs
StatePublished - 1 Dec 2012
Externally publishedYes

Keywords

  • Bioinformatics
  • Machine learning
  • Markov models
  • Named entity recognition
  • Natural language processing

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