title: Refinement of an automatic method for indexing medical literature--a preliminary study.
abstract: OBJECTIVE
  to rank according to their significance MeSH terms automatically extracted from Internet sites in the framework of a French project, VUMeF, a contribution to the NLM' UMLS project.
  
  
  METHODS
  scores are affected to key-words of a given document on the basis of the Semantic Network of the UMLS and frequencies of co-occurring major terms in the Medline literature. If N is the number of major terms of a document, and n is the number of major terms retrieved in the N first terms ranked in descending order according to their scores, the measure of the achievement of the method is n/N.
  
  
  RESULTS
  a set of 1444 randomized documents have been extracted from Medline. For each document we computed the retrieved major terms among the first N terms with two methods: a statistical method using only frequencies given by co-occurrences, and our method that uses furthermore the UMLS semantic network. In 34% of cases corresponding to documents indexed by about 16 key-words, about 3 major terms among them, our method produces a better precision (7%) than the statistical method.
  
  
  CONCLUSIONS
  the rough calculation of the proportion of retrieved major terms should be enhanced by the use of a probability law allowing to enlarge the list of terms to select taking into account both the number of major terms and the total number of key-words used to index each document.
authors.affiliation: LERTIM, Faculté de Médecine, Université de la Méditerranée, Marseille, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Joubert
authors.forename: Anne-Laure
authors.initials: AL
authors.lastname: Peretti
authors.forename: Joanny
authors.initials: J
authors.lastname: Gouvernet
authors.forename: Marius
authors.initials: M
authors.lastname: Fieschi
mesh_terms: D006801:Humans; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing; D012660:Semantics; D017432:Unified Medical Language System