Distributional correspondence indexing for cross-language text categorization (Contributo in atti di convegno)

Type
Label
  • Distributional correspondence indexing for cross-language text categorization (Contributo in atti di convegno) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-319-16354-3_12 (literal)
Alternative label
  • Esuli A., Fernandez A.M. (2015)
    Distributional correspondence indexing for cross-language text categorization
    in ECIR 2015 - Advances in Information Retrieval. 37th European Conference on IR Research, Vienna, Austria, 29 March - 2 April 2015
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Esuli A., Fernandez A.M. (literal)
Pagina inizio
  • 104 (literal)
Pagina fine
  • 109 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/chapter/10.1007%2F978-3-319-16354-3_12 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 9022 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 9022 (literal)
Rivista
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy (literal)
Titolo
  • Distributional correspondence indexing for cross-language text categorization (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-319-16353-6 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Allan Hanbury, Gabriella Kazai, Andreas Rauber, Norbert Fuhr (literal)
Abstract
  • Cross-Language Text Categorization (CLTC) aims at producing a classifier for a target language when the only available training examples belong to a different source language. Existing CLTC methods are usually affected by high computational costs, require external linguistic resources, or demand a considerable human annotation effort. This paper presents a simple, yet effective, CLTC method based on projecting features from both source and target languages into a common vector space, by using a computationally lightweight distributional correspondence profile with respect to a small set of pivot terms. Experiments on a popular sentiment classification dataset show that our method performs favorably to state-of-the-art methods, requiring a significantly reduced computational cost and minimal human intervention. (literal)
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