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首页> 外文期刊>ACM transactions on Asian language information processing >Estimating Satisfactoriness of Selectional Restriction from Corpus without a Thesaurus
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Estimating Satisfactoriness of Selectional Restriction from Corpus without a Thesaurus

机译：估计没有词库的语料库中选择限制的满意度

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摘要

A selectional restriction specifies what combinations of words are semantically valid in a particular syntactic construction. This is one of the basic and important pieces of knowledge in natural language processing and has been used for syntactic and word sense disambiguation. In the case of acquiring the selectional restriction for many combinations of words from a corpus, it is necessary to estimate whether or not a word combination that is not observed in the corpus satisfies the selectional restriction. This paper proposes a new method for estimating the degree of satisfaction of the selectional restriction for a word combination from a tagged corpus, based on the multiple regression model. The independent variables of this model correspond to modifiers. Unlike a conventional multiple regression analysis, the independent variables are also parameters to be learned. We experiment on estimating the degree of satisfaction of the selectional restriction for Japanese word combinations < noun, postpositional-particle, verb >. The experimental results indicate that our method estimates the degree of satisfaction of a word combination not very well observed in the corpus, and that the accuracy of syntactic disambiguation using the co-occurrencies estimated by our method is higher than using co-occurrence probabilities smoothed by previous methods.
机译：选择限制指定在特定句法构造中哪些单词组合在语义上有效。这是自然语言处理中基础知识和重要知识之一，已用于句法和词义歧义消除。在从语料库获取针对单词的许多组合的选择限制的情况下，需要估计在语料库中未观察到的单词组合是否满足选择限制。基于多元回归模型，本文提出了一种新的方法，用于估计标记语料库中单词组合的选择限制的满足程度。该模型的自变量对应于修饰符。与传统的多元回归分析不同，自变量也是要学习的参数。我们尝试估算日语单词组合<名词，后置词，动词>的选择限制的满意度。实验结果表明，我们的方法估计的是在语料库中观察不到的单词组合的满意程度，并且使用通过我们的方法估计的共现概率，句法歧义消除的准确度要高于使用经方法平滑的共现概率。以前的方法。

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