Istituto di Scienza e Tecnologie dell'Informazione     
Esuli A., Moreo Fernāndez A. 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). Proceedings, pp. 104 - 109. Allan Hanbury, Gabriella Kazai, Andreas Rauber, Norbert Fuhr (eds.). (Lecture Notes in Computer Science, vol. 9022). Springer, 2015.
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.
URL: http://link.springer.com/chapter/10.1007%2F978-3-319-16354-3_12
DOI: 10.1007/978-3-319-16354-3_12
Subject Cross-Language Text Categorization
Distributional Semantics
Sentiment Analysis
I.2.7 Natural Language Processing
I.2.6 Learning

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