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arXiv · 1804.09238

Semi-Supervised Learning with Declaratively Specified Entropy Constraints

Haitian Sun·William W. Cohen·Lidong Bing
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Abstract

We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). The proposed method can be used to specify ensembles of semi-supervised learning, as well as agreement constraints and entropic regularization constraints between these learners, and can be used to model both well-known heuristics such as co-training and novel domain-specific heuristics. In addition to representing individual SSL heuristics, we show that multiple heuristics can also be automatically combined using Bayesian optimization methods. We show consistent improvements on a suite of well-studied SSL benchmarks, including a new state-of-the-art result on a difficult relation extraction task.

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Haitian Sun, William W. Cohen, Lidong Bing. 2018-04-24. Semi-Supervised Learning with Declaratively Specified Entropy Constraints. https://arxiv.org/abs/1804.09238

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