In cross-domain text classification, topic labels for documents of a target domain are predicted by leveraging knowledge of labeled documents of a source domain, having equal or similar topics with possibly different words. Existing methods either adapt documents of the source domain to the target or represent both domains in a common space. These methods are mostly based on advanced statistical techniques and often require tuning of parameters in order to obtain optimal performances. We propose a more straightforward approach based on nearest centroid classification: profiles of topic categories are extracted from the source domain and are then adapted by iterative refining steps using most similar documents in the target domain. Experiments on common benchmark datasets show that this approach, despite its simplicity, obtains accuracy measures better or comparable to other methods, obtained with fixed empirical values for its few parameters.

Iterative Refining of Category Profiles for Nearest Centroid Cross-Domain Text Classification

DOMENICONI, GIACOMO;MORO, GIANLUCA;PASOLINI, ROBERTO;SARTORI, CLAUDIO
2015

Abstract

In cross-domain text classification, topic labels for documents of a target domain are predicted by leveraging knowledge of labeled documents of a source domain, having equal or similar topics with possibly different words. Existing methods either adapt documents of the source domain to the target or represent both domains in a common space. These methods are mostly based on advanced statistical techniques and often require tuning of parameters in order to obtain optimal performances. We propose a more straightforward approach based on nearest centroid classification: profiles of topic categories are extracted from the source domain and are then adapted by iterative refining steps using most similar documents in the target domain. Experiments on common benchmark datasets show that this approach, despite its simplicity, obtains accuracy measures better or comparable to other methods, obtained with fixed empirical values for its few parameters.
Knowledge Discovery, Knowledge Engineering and Knowledge Management- 6th International Joint Conference, IC3K 2014, Rome, Italy, October21-24, 2014, Revised Selected Papers
50
67
Domeniconi, Giacomo; Moro, Gianluca; Pasolini, Roberto; Sartori, Claudio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/555363
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