Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
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DOI：
10.5555/972705.972707
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发表时间：
1997-09
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影响因子：
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通讯作者：
Dekai Wu
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作者：
Dekai Wu
我们介绍了(1)一种新的用于句子对的双语语言建模的随机倒置转换文法，以及(2)双语句法的概念以及各种并行语料库分析的应用。除了双语取向，形式主义与计算语言学中更传统的有限状态换能器有三个主要特征：它直接跳到上下文无关而不是有限状态基，它允许最小程度的额外排序灵活性，其概率公式允许高效的最大似然双语分析算法。证明了存在一种方便的范式。对形式主义的表现力的分析表明，它特别适合于建模语言之间的顺序转换，平衡所需的灵活性和复杂性约束。我们讨论了一些例子，说明随机倒置转换语法如何将双语约束应用于有问题的语料库分析任务，如切分、括号、短语对齐和句法分析。
We introduce (1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs, and (2) the concept of bilingual parsing with a variety of parallel corpus analysis applications. Aside from the bilingual orientation, three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics: it skips directly to a context-free rather than finite-state base, it permits a minimal extra degree of ordering flexibility, and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm. A convenient normal form is shown to exist. Analysis of the formalism's expressiveness suggests that it is particularly well suited to modeling ordering shifts between languages, balancing needed flexibility against complexity constraints. We discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks such as segmentation, bracketing, phrasal alignment, and parsing.