Abstract
In this paper, we propose a new unsupervised domain adaptation method for document classification. We address the problem of domain adaptation for document classification where the source and target domains do not differ significantly and there is no labeled data in the target domain. In this case, we can use conventional semi-supervised learning. Thus, we use the naive Bayes-based expectation-maximization method (NBEM) which is very effective for document classification. However, NBEM does not utilize the difference between a source domain and a target domain. We combine NBEM with the feature weighted method for domain adaptation, referred to as “self-training feature weight” (STFW). Our proposed method alternately uses NBEM and STFW to gradually improve document classification precision for a target domain. This method significantly outperforms the conventional unsupervised methods for domain adaptation.
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Notes
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This equation is smoothed by considering the frequency 0.
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We set these values according to the result of preliminary experiment.
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Acknowledgment
The work reported in this article was supported by the NINJAL collaborative research project ‘Development of all-words WSD systems and construction of a correspondence table between WLSP and IJD by these systems.’
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Shinnou, H., Komiya, K., Sasaki, M. (2018). Domain Adaptation for Document Classification by Alternately Using Semi-supervised Learning and Feature Weighted Learning. In: Hasida, K., Pa, W. (eds) Computational Linguistics. PACLING 2017. Communications in Computer and Information Science, vol 781. Springer, Singapore. https://doi.org/10.1007/978-981-10-8438-6_17
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