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.

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

Moreo Fernandez A;Esuli A;Sebastiani F
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.
2017
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Sentiment classification
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Descrizione: Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/339845
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