Constituent Parsing with Incremental Sigmoid Belief Networks
Constituent Parsing with Incremental Sigmoid Belief Networks
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发表时间：
2007-06
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通讯作者：
Ivan Titov;James Henderson
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作者：
Ivan Titov;James Henderson
我们介绍了一个框架的基础上的动态Sigmoid信念网络的形式称为增量Sigmoid信念网络的潜在变量的句法分析。我们证明，以前的前馈神经网络解析模型可以被看作是一个粗略的近似推理与这一类的图形模型。通过构建一个更准确，但仍然听话的近似，我们显着提高解析精度，这表明ISBN提供了一个很好的理想化解析。这种生成式解析模型在WSJ文本上实现了最先进的结果，并且比基线神经网络解析器减少了8%的错误。
We introduce a framework for syntactic parsing with latent variables based on a form of dynamic Sigmoid Belief Networks called Incremental Sigmoid Belief Networks. We demonstrate that a previous feed-forward neural network parsing model can be viewed as a coarse approximation to inference with this class of graphical model. By constructing a more accurate but still tractable approximation, we significantly improve parsing accuracy, suggesting that ISBNs provide a good idealization for parsing. This generative model of parsing achieves state-of-theart results on WSJ text and 8% error reduction over the baseline neural network parser.