title: Building a protein name dictionary from full text: a machine learning term extraction approach.
abstract: BACKGROUND
  The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature.
  
  
  RESULTS
  We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text.
  
  
  CONCLUSIONS
  This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.
authors.affiliation: Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. les2007@med.cornell.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Shi
authors.forename: Fabien
authors.initials: F
authors.lastname: Campagne
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D015706:Bibliometrics; D019295:Computational Biology; D016206:Databases, Bibliographic; D030541:Databases, Genetic; D030562:Databases, Protein; D004014:Dictionaries as Topic; D006801:Humans; D033181:Information Dissemination; D007255:Information Services; D016247:Information Storage and Retrieval; D016239:MEDLINE; D009275:Names; D009323:Natural Language Processing; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D039781:PubMed; D018511:Systems Integration; D009626:Terminology as Topic; D014584:User-Computer Interface
chemical_list: D011506:Proteins