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

Giacomo Domeniconi, Gianluca Moro, Roberto Pasolini, Claudio Sartori
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, Communications in Computer and Information Science 553, pages 50-67, 2015.
Ana Fred, Jan L. G. Dietz, David Aveiro, Kecheng Liu, Joaquim Filipe (eds.), Springer International Publishing
@article{,
	booktitle = {Knowledge Discovery, Knowledge Engineering and Knowledge Management},
	year = 2015,
	status = {Published},
	url = {http://dx.doi.org/10.1007/978-3-319-25840-9_4},
	editor = {Fred, Ana and Dietz, Jan L. G. and Aveiro, David and Liu, Kecheng and Filipe, Joaquim},
	series = {Communications in Computer and Information Science},
	publisher = {Springer International Publishing},
	author = {Domeniconi, Giacomo and Moro, Gianluca and Pasolini, Roberto and Sartori, Claudio},
	title = {Iterative Refining of Category Profiles for Nearest Centroid Cross-Domain Text Classification},
	isbn = {978-3-319-25839-3},
	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.},
	pages = {50-67},
	volume = 553,
	doi = {10.1007/978-3-319-25840-9_4}}