Judging Grammaticality with Tree Substitution Grammar Derivations
Judging Grammaticality with Tree Substitution Grammar Derivations
复制标题
用树替换语法推导判断语法
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发表时间：
2011
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通讯作者：
Matt Post
中科院分区：
文献类型：
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作者：
Matt Post
在本文中，我们表明，本地功能计算的衍生树替代语法-如识别特定的片段，和计数的大小片段-是有用的二进制语法分类任务。这样的特征远远优于n-gram特征和各种模型分数。尽管它们的性能不及Charniak和约翰逊（2005）为解析树重新排序而开发的手工特征集，但它们使用的特征数量级更少。此外，由于所采用的TSG是在贝叶斯设置中学习的，因此可以将其推导的使用视为对分类有用的树模式的自动发现。在BLLIP数据集上，我们在区分语法文本和来自n-gram语言模型的样本方面实现了89.9%的准确率。
In this paper, we show that local features computed from the derivations of tree substitution grammars --- such as the identify of particular fragments, and a count of large and small fragments --- are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. Furthermore, since the TSGs employed are learned in a Bayesian setting, the use of their derivations can be viewed as the automatic discovery of tree patterns useful for classification. On the BLLIP dataset, we achieve an accuracy of 89.9% in discriminating between grammatical text and samples from an n-gram language model.