We introduce a novel technique called dynamic feature induction that keeps inducing high dimensional features automatically until the feature space becomes `more' linearly separable. Dynamic feature induction searches for the feature combinations that give strong clues for distinguishing certain label pairs, and generates joint features from these combinations. These induced features are trained along with the primitive low dimensional features. Our approach was evaluated on two core NLP tasks, part-of-speech tagging and named entity recognition, and showed the state-of-the-art results for both tasks, achieving the accuracy of 97.64 and the F1-score of 91.00 respectively, with about a 25% increase in the feature space.
Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) / 2016
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