Abstract
In this chapter, we briefly touch on topics that may increase in importance for text mining, but are not yet central to prediction. These include summarization, active learning, learning with unlabeled data, learning with multiple samples or models, online learning, deep learning, cost-sensitive learning, unbalanced samples and rare events, distributed text mining, rank learning and question answering.
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© 2015 Springer-Verlag London
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Weiss, S.M., Indurkhya, N., Zhang, T. (2015). Emerging Directions. In: Fundamentals of Predictive Text Mining. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-6750-1_9
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DOI: https://doi.org/10.1007/978-1-4471-6750-1_9
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