title: Creating knowledgebases to text-mine PUBMED articles using clustering techniques.
abstract: Knowledgebase-mediated text-mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing
authors.affiliation: Center for Medical Informatics, Yale University, New Haven, Connecticut, USA.
authors.forename: Chiquito J
authors.initials: CJ
authors.lastname: Crasto
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Morse
authors.forename: Michele
authors.initials: M
authors.lastname: Migliore
authors.forename: Prakash
authors.initials: P
authors.lastname: Nadkarni
authors.forename: Michael
authors.initials: M
authors.lastname: Hines
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Brash
authors.forename: Perry L
authors.initials: PL
authors.lastname: Miller
authors.forename: Gordon M
authors.initials: GM
authors.lastname: Shepherd
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019992:Databases as Topic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009462:Neurology; D039781:PubMed