Discriminative reranking for natural language parsing
Discriminative reranking for natural language parsing
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DOI：
10.1162/0891201053630273
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发表时间：
2005-03-01
影响因子：
9.3
通讯作者：
Koo, T
中科院分区：
文献类型：
--
作者：
Collins, M;Koo, T
本文考虑对现有概率解析器的输出进行重新排序的方法。基本解析器为每个输入句子生成一组候选解析，以及定义这些解析的初始排名的相关概率。然后，第二个模型尝试使用树的其他特征作为证据来改进此初始排名。我们方法的优点在于，它允许将树表示为任意特征集，而不用担心这些特征如何交互或重叠，也不需要定义考虑这些特征的推导或生成模型。我们基于 Freund 等人描述的排名问题的提升方法，引入了一种用于重新排名任务的新方法。 （1998）。我们应用提升方法来解析华尔街日报树库。该方法将基线模型（Collins [19991）的对数似然与来自解析树上未包含在原始模型中的额外 500,000 个特征的证据相结合。新模型的 F 测量得分为 89.75%，与基线模型的 88.2% 得分相比，F 测量误差相对降低了 13%。本文还介绍了一种新的 boosting 算法，该算法利用了解析数据中特征空间的稀疏性。实验表明，与明显的提升方法实现相比，新算法的效率显着提高。我们认为，该方法在简单性和效率方面是一种有吸引力的替代方案，可用于对数线性（最大熵）模型中的特征选择方法。尽管本文中的实验是针对自然语言解析 (NLP) 的，但该方法应该适用于许多其他自然构建为排名任务的 NLP 问题，例如语音识别、机器翻译或自然语言生成。
This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). We apply the boosting method to parsing the Wall Street journal treebank. The method combined the log-likelihood under a baseline model (that of Collins [19991) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model's score of 88.2%. The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative-in terms of both simplicity and efficiency-to work on feature selection methods within log-linear (maximum-entropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.