Competitive generative models with structure learning for NLP classification tasks
Competitive generative models with structure learning for NLP classification tasks
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DOI：
10.3115/1610075.1610155
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发表时间：
2006-07
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影响因子：
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通讯作者：
Kristina Toutanova
中科院分区：
文献类型：
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作者：
Kristina Toutanova
在本文中，我们表明，生成模型的竞争力，有时上级判别模型，当这两种模型都允许学习的结构是最佳的歧视。特别是，我们比较了贝叶斯网络和条件对数线性模型在两个NLP任务。我们观察到，当生成模型的结构编码非常强的独立性假设时（一种朴素贝叶斯），判别模型是上级的，但是当生成模型被允许通过学习更复杂的结构来削弱这些独立性假设时，它可以实现与相应的判别模型非常相似或更好的性能。此外，由于生成模型的结构学习效率要高得多，因此它们可能更适合某些任务。
In this paper we show that generative models are competitive with and sometimes superior to discriminative models, when both kinds of models are allowed to learn structures that are optimal for discrimination. In particular, we compare Bayesian Networks and Conditional loglinear models on two NLP tasks. We observe that when the structure of the generative model encodes very strong independence assumptions (a la Naive Bayes), a discriminative model is superior, but when the generative model is allowed to weaken these independence assumptions via learning a more complex structure, it can achieve very similar or better performance than a corresponding discriminative model. In addition, as structure learning for generative models is far more efficient, they may be preferable for some tasks.