A discriminative language model with pseudo-negative samples

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A discriminative language model with pseudo-negative samples
In this paper, we propose a novel discriminative language model, which can be applied quite generally. Compared to the well known N-gram language models, discriminative language models can achieve more accurate discrimination because they can employ overlapping features and nonlocal information. However, discriminative language models have been used only for re-ranking in specific applications because negative examples are not available. We propose sampling pseudo-negative examples taken from probabilistic language models. However, this approach requires prohibitive computational cost if we are dealing with quite a few features and training samples. We tackle the problem by estimating the latent information in sentences using a semiMarkov class model, and then extracting features from them. We also use an online margin-based algorithm with efficient kernel computation. Experimental results show that pseudo-negative examples can be treated as real negative examples and our model can ...
Daisuke Okanohara, Jun-ichi Tsujii
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ACL
Authors Daisuke Okanohara, Jun-ichi Tsujii
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