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Exploring the Context of Lexical Functions

机译:探索词法功能的上下文

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摘要

We explore the context of verb-noun collocations using a corpus of the Excelsior newspaper issues in Spanish. Our purpose is to understand to what extent the context is able to distinguish the semantics of collocations represented by lexical functions of the Meaning-Text Theory. For experiments, four lexical functions were chosen: Operl, Reall, CausFuncO, and CausFuncl. We inspected different parts of the eight-word window context: the left context, the right context, and both the left and right context. These contexts were retrieved from the original corpus as well as from the same corpus after stopwords deletion. For the vector representation of the context, word counts and tf-idf of words were used. To estimate the ability of the context to predict lexical functions, we used various machine-learning techniques. The best F-measure of 0.65 was achieved for predicting Reall by Gaussian Naieve Bayes using the left context without stopwords and word counts as features in vectors.
机译:我们使用西班牙语的Excelsior报纸语料库探索动词-名词搭配的上下文。我们的目的是了解上下文在多大程度上能够区分由意义文本理论的词汇功能表示的搭配的语义。对于实验,选择了四个词法功能:Operl,Reall,CausFuncO和CausFuncl。我们检查了八字窗口上下文的不同部分:左侧上下文,右侧上下文以及左侧和右侧上下文。这些上下文是从原始语料库以及停用词删除后的同一语料库中检索到的。对于上下文的向量表示,使用了单词计数和单词的tf-idf。为了估计上下文预测词汇功能的能力,我们使用了各种机器学习技术。高斯·奈夫·贝叶斯使用左上下文而不使用停用词和单词计数作为向量中的特征来预测Reall时,获得了0.65的最佳F度量。

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