Structured Output Learning with Polynomial Kernel
Hajime Morita, Hiroya Takamura, Manabu Okumura · Recent Advances in Natural Language Processing · 2009
We propose a new method which enables the training of a kernelized structured output model. The structured output learning can flexibly represent a problem, and thus is gaining popularity in natural language processing. Meanwhile the polynomial kernel method is effective in many natural language processing tasks, since it takes into account the combination of features. However, it is computationally difficult to simultaneously use both the structured output learning and the kernel method. Our method avoids this difficulty by transforming the kernel function, and enables the kernelized structured output learning. We theoretically discuss the computational complexity of the proposed method and also empirically show its high efficiency and effectiveness through experiments in the task of identifying agreement and disagreement relations between utterances in meetings. Identifying agreement and disagreement relations consists of two mutuallycorrelated problems: identification of the utterance which each utterance is intended for, and classification of each utterance into approval, disapproval or others. We simultaneously use both of the structured output learning and the kernel method in order to take into account this correlation of the two problems.