Automatic expansion of domain-specific lexicons by term categorization (Articolo in rivista)

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  • Automatic expansion of domain-specific lexicons by term categorization (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1138379.1138380 (literal)
Alternative label
  • Avancini H.; Lavelli A.; Sebastiani F.; Zanoli R. (2006)
    Automatic expansion of domain-specific lexicons by term categorization
    in ACM transactions on speech and language processing (Online); ACM Press, New York (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Avancini H.; Lavelli A.; Sebastiani F.; Zanoli R. (literal)
Pagina inizio
  • 1 (literal)
Pagina fine
  • 30 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?doid=1138379.1138380 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: ACM Transactions on Speech and Language Processing, vol. 3 (1) pp. 1-30. ACM, 2006. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 30 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • - Avancini e Sebastiani: Istituto di Scienza e Tecnologie dell'Informazione, Consiglio Nazionale delle Ricerche - Lavelli e Zanoli: Istituto per la Ricerca Scientifica e Tecnologica, Istituto Trentino di Cultura (ora Fondazione Bruno Kessler) (literal)
Titolo
  • Automatic expansion of domain-specific lexicons by term categorization (literal)
Abstract
  • We discuss an approach to the automatic expansion of domain-specific lexicons, i.e., to the problem of extending, for each ci in a predefined set C = {c1, . . . , cm} of semantic domains, an initial lexicon Li 0 into a larger lexicon Li 1. Our approach relies on term categorization, defined as the task of labeling previously unlabeled terms according to a predefined set of domains. We approach this as a supervised learning problem, in which term classifiers are built using the initial lexicons as training data. Dually to classic text categorization tasks, in which documents are represented as vectors in a space of terms, we represent terms as vectors in a space of documents. We present the results of a number of experiments in which we use a boosting-based learning device for training our term classifiers. We test the effectiveness of our method by using WordNetDomains, a well-known large set of domain-specific lexicons, as a benchmark. Our experiments are performed using the documents in the Reuters Corpus Volume 1 as 'implicit' representations for our terms. (literal)
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