Unsupervised gene/protein named entity normalization using automatically extracted dictionaries

Research output: Contribution to conferencePaperpeer-review

47 Scopus citations

Abstract

Gene and protein named-entity recognition (NER) and normalization is often treated as a two-step process. While the first step, NER, has received considerable attention over the last few years, normalization has received much less attention. We have built a dictionary based gene and protein NER and normalization system that requires no supervised training and no human intervention to build the dictionaries from online genomics resources. We have tested our system on the Genia corpus and the BioCreative Task 1B mouse and yeast corpora and achieved a level of performance comparable to state-of-the-art systems that require supervised learning and manual dictionary creation. Our technique should also work for organisms following similar naming conventions as mouse, such as human. Further evaluation and improvement of gene/protein NER and normalization systems is somewhat hampered by the lack of larger test collections and collections for additional organisms, such as human.

Original languageEnglish (US)
Pages17-24
Number of pages8
StatePublished - 2005
Event2005 ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics, ACL-ISMB 2005 - Detroit, United States
Duration: Jun 24 2005 → …

Conference

Conference2005 ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics, ACL-ISMB 2005
Country/TerritoryUnited States
CityDetroit
Period6/24/05 → …

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence
  • Information Systems

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