title: Text categorization of biomedical data sets using graph kernels and a controlled vocabulary.
abstract: Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers.
authors.affiliation: New Jersey Institute of Technology, Newark.
authors.forename: Said
authors.initials: S
authors.lastname: Bleik
authors.affiliation: University of Kansas, Lawrence.
authors.forename: Meenakshi
authors.initials: M
authors.lastname: Mishra
authors.affiliation: University of Kansas, Lawrence.
authors.forename: Jun
authors.initials: J
authors.lastname: Huan
authors.affiliation: Yonsei University, Seol.
authors.forename: Min
authors.initials: M
authors.lastname: Song
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D008373:Manuscripts as Topic; D009323:Natural Language Processing; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled