Parameter Estimation for Statistical Parsing Models: Theory and Practice of
Parameter Estimation for Statistical Parsing Models: Theory and Practice of
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DOI：
10.1007/1-4020-2295-6_2
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发表时间：
2001
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影响因子：
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通讯作者：
M. Collins
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文献类型：
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作者：
M. Collins
统计分析中的一个基本问题是用于估计模型中参数的标准和算法的选择。计算语言学中的主要方法是使用参数模型和一些最大似然估计的变体。最大似然估计被证明是合理的假设可以说是相当强的。本章讨论了各种参数估计方法的统计理论基础，并给出了依赖于（平滑）最大似然估计的算法。首先，我们对统计学习理论的结果进行了概述。然后，我们展示了如何重要的概念，从分类文献，具体来说，泛化结果的基础上利润率的训练数据，可以推导出解析模型。最后，我们描述了参数估计算法，这些推广界的动机。
A fundamental problem in statistical parsing is the choice of criteria and algorithms used to estimate the parameters in a model. The predominant approach in computational linguistics has been to use a parametric model with some variant of maximum-likelihood estimation. The assumptions under which maximumlikelihood estimation is justified are arguably quite strong. This chapter discusses the statistical theory underlying various parameter-estimation methods, and gives algorithms which depend on alternatives to (smoothed) maximumlikelihood estimation. We first give an overview of results from statistical learning theory. We then show how important concepts from the classification literature-specifically, generalization results based on margins on training data–can be derived for parsing models. Finally, we describe parameter estimation algorithms which are motivated by these generalization bounds.