McKnight and Srinivasan ( 2003 ) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques .
categorization of sentence types in medical abstracts
Larry McKnight Padmini Srinivasan
Furthermore , the availability of rich ontological resources , in the form of the Unified Medical Language System ( UMLS ) ( Lindberg et al. , 1993 ) , and the availability of software that leverages this knowledge -- MetaMap ( Aronson , 2001 ) for concept identification and SemRep ( Rindflesch and Fiszman , 2003 ) for relation extraction -- provide a foundation for studying the role of semantics in various tasks . McKnight and Srinivasan ( 2003 ) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques . Building on the work of Ruch et al. ( 2003 ) in the same domain , we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models ( HMMs ) ; cfXXX ( Barzilay and Lee , 2004 ) .
@@CITATION have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques .