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Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification

Articolo
Data di Pubblicazione:
2017
Abstract:
Researchers from ISTI-CNR, Pisa (in a joint effort with the Qatar Computing Research Institute), have developed a transfer learning method that allows cross-domain and cross-lingual sentiment classification to be performed accurately and efficiently. This means sentiment classification efforts can leverage training data originally developed for performing sentiment classification on other domains and/or in other languages.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
Sentiment classification
Elenco autori:
MOREO FERNANDEZ, Alejandro; Esuli, Andrea; Sebastiani, Fabrizio
Autori di Ateneo:
ESULI ANDREA
MOREO FERNANDEZ ALEJANDRO DAVID
SEBASTIANI FABRIZIO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/339845
Link al Full Text:
https://iris.cnr.it//retrieve/handle/20.500.14243/339845/180978/prod_376280-doc_127047.pdf
Pubblicato in:
ERCIM NEWS
Journal
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URL

https://ercim-news.ercim.eu/en111/r-i/distributional-correspondence-indexing-for-cross-lingual-and-cross-domain-sentiment-classification
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