Abstract
This paper proposes a semi-supervised latent Dirichlet allocation (ssLDA) method, which differs from the existing supervised topic models for multi-label classification in mainly two aspects. Firstly both labeled and unlabeled learning data are used in ssLDA to train a model, which is very important for reducing the cost by manually labeling, especially when obtaining a fully labeled dataset is difficult. Secondly ssLDA provides a more flexible training scheme that allows two ways of labeling assignment while existing topic model-based methods usually focus on either of them: (1) a document-level assignment of labels to a document; (2) imposing word-level correspondences between words and labels within a document. Our experiment results indicate that ssLDA gains an advantage over other methods in implementation flexibility and can outperform others in terms of multi-label classification performance.
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Lu, Y., Okada, S., Nitta, K. (2013). Semi-supervised Latent Dirichlet Allocation for Multi-label Text Classification. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_36
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DOI: https://doi.org/10.1007/978-3-642-38577-3_36
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