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ClassiNet -- Predicting Missing Features for Short-Text Classification
[article]
2018
arXiv
pre-print
The fundamental problem in short-text classification is feature sparseness -- the lack of feature overlap between a trained model and a test instance to be classified. We propose ClassiNet -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance.
arXiv:1804.05260v1
fatcat:b3ev546g5jgvxo4noo5nsd7xii