On performance of Topical Opinion Retrieval (Contributo in atti di convegno)

Type
Label
  • On performance of Topical Opinion Retrieval (Contributo in atti di convegno) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1835449.1835611 (literal)
Alternative label
  • Giambattista Amati; Giuseppe Amodeo; Valerio Capozio; Carlo Gaibisso; Giorgio Gambosi (2010)
    On performance of Topical Opinion Retrieval
    in The 33rd international ACM SIGIR conference on Research and development in information retrieval, Geneva, Switzerland, july 19-23, 2010
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Giambattista Amati; Giuseppe Amodeo; Valerio Capozio; Carlo Gaibisso; Giorgio Gambosi (literal)
Pagina inizio
  • 777 (literal)
Pagina fine
  • 778 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR (2010) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • ACM SIGIR Conference (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 2 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Giambattista Amati: Fondazione Ugo Bordoni, Rome, Italy Giuseppe Amodeo: University of L'Aquila, L'Aquila, Italy Valerio Capozio: University \"Tor Vergata\", Rome, Italy Carlo Gaibisso: IASI - CNR, Rome, Italy Giorgio Gambosi: University \"Tor Vergata\", Rome, Italy (literal)
Titolo
  • On performance of Topical Opinion Retrieval (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0153-4 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Fabio Crestani; Stephane Marchand-Maillet; Hsin-Hsi Chen; Efthimis N. Efthimiadis; Jacques Savoy (literal)
Abstract
  • We investigate the effectiveness of both the standard evaluation measures and the opinion component for topical opinion retrieval. We analyze how relevance is affected by opinions by perturbing relevance ranking by the outcomes of opinion-only classiffiers built by Monte Carlo sampling. Topical opinion rankings are obtained by either re-ranking or filtering the documents of a rst-pass retrieval of topic relevance. The proposed approach establishes the correlation between the accuracy and the precision of the classier and the performance of the topical opinion retrieval. Among other results, it is possible to assess the effectiveness of the opinion component by comparing the effectiveness of the relevance baseline with the topical opinion ranking (literal)
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