Boosting Text Segmentation via Progressive Classification (Articolo in rivista)

Type
Label
  • Boosting Text Segmentation via Progressive Classification (Articolo in rivista) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s10115-007-0085-3 (literal)
Alternative label
  • Cesario Eugenio; Folino Francesco Paolo; Locane Antonio; Manco Giuseppe; Ortale Riccardo (2008)
    Boosting Text Segmentation via Progressive Classification
    in Knowledge and Information Systems; Springer-Verlag London Limited, London (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Cesario Eugenio; Folino Francesco Paolo; Locane Antonio; Manco Giuseppe; Ortale Riccardo (literal)
Pagina inizio
  • 285 (literal)
Pagina fine
  • 320 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/y845732790133726/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 15 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 36 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
  • DBLP (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR; ICAR-CNR; ICAR-CNR; ICAR-CNR; ICAR-CNR (literal)
Titolo
  • Boosting Text Segmentation via Progressive Classification (literal)
Abstract
  • A novel approach for reconciling tuples stored as free text into an existing attribute schema is proposed. The basic idea is to subject the available text to progressive classification, i.e., a multi-stage classification scheme where, at each intermediate stage, a classifier is learnt that analyzes the textual fragments not reconciled at the end of the previous steps. Classifica- tion is accomplished by an ad hoc exploitation of traditional association mining algorithms, and is supported by a data transformation scheme which takes advantage of domain-specific dictionaries/ontologies. A key feature is the capability of progressively enriching the avail- able ontology with the results of the previous stages of classification, thus significantly improving the overall classification accuracy. An extensive experimental evaluation shows the effectiveness of our approach. (literal)
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