Abstract
Here we propose a novel approach for the task of domain adaptation for Natural Language Processing. Our approach captures relations between the source and target domains by applying a model transformation mechanism which can be learnt by using labeled data of limited size taken from the target domain. Experimental results on several Opinion Mining datasets show that our approach significantly outperforms baselines and published systems when the amount of labeled data is extremely small.
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Ormándi, R., Hegedűs, I., Farkas, R. (2010). Opinion Mining by Transformation-Based Domain Adaptation. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_21
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DOI: https://doi.org/10.1007/978-3-642-15760-8_21
Publisher Name: Springer, Berlin, Heidelberg
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