Information extraction from full text scientific articles: where are the keywords?
BACKGROUND
To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.


RESULTS
In this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous.


CONCLUSIONS
Although the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.
Shah
Parantu K
PK
Biocomputing, European Molecular Biology Laboratory, Heidelberg, Germany. shah@embl.de
Perez-Iratxeta
Carolina
C
Bork
Peer
P
Andrade
Miguel A
MA
D000715:Anatomy / Q000145:classification / Q000706:statistics & numerical data; D000828:Animal Population Groups / Q000145:classification; D000818:Animals; D001419:Bacteria / Q000145:classification; D005823:Genetics / Q000145:classification / Q000706:statistics & numerical data; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods* / Q000706:statistics & numerical data / Q000639:trends*; D007256:Information Systems / Q000592:standards / Q000706:statistics & numerical data / Q000639:trends*; D020407:Internet; D009862:Online Systems; D010506:Periodicals as Topic / Q000706:statistics & numerical data / Q000639:trends*; D010944:Plants / Q000145:classification; D040901:Proteomics / Q000145:classification / Q000706:statistics & numerical data; D015983:Selection Bias; D013045:Species Specificity; D009626:Terminology as Topic; D018875:Vocabulary, Controlled