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Classifying the lexico-syntactic patterns of semantic relations between two nouns in Romanian language

机译:用罗马尼亚语对两个名词之间的语义关系的词汇句法模式进行分类

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A very important step toward the goal of human-computer dialog using natural language is the identification of semantic relations between different constituents of texts or speech. The semantic relations that can be established between the words senses are related to their part-of-speech. Because the noun is one of the most important lexical categories we focused on the semantic relations encoded by a lexico-syntactic pattern between two nouns. The identification of the syntactic dependencies established between the two nouns in Romanian language is strong correlated with the lexico-syntactic patterns that contain them. In this paper we present the results of these patterns classification using different lexico-syntactic features and three supervised learning methods (decision trees, Na??ve Bayes and k-nearest neighbors).
机译:朝着使用自然语言的人机对话目标迈出的非常重要的一步是识别文本或语音的不同组成部分之间的语义关系。词义之间可以建立的语义关系与其词性有关。因为名词是最重要的词汇类别之一,所以我们重点研究由两个名词之间的词汇句法模式编码的语义关系。罗马尼亚语中两个名词之间建立的句法依存关系的确定与包含它们的词汇-句法模式密切相关。在本文中,我们介绍了使用不同的词汇语法特征和三种监督学习方法(决策树,朴素贝叶斯和k最近邻)对这些模式进行分类的结果。

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