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First approach toward Semantic Role Labeling for Basque

机译:巴斯克语语义角色标记的第一种方法

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In this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the Processing of Basque (EPEC). In our experiments the classifier that offers the best results is based on Support Vector Machines. Our system achieves 84.30 F_1 score in identifying the PropBank semantic role for a given constituent and 82.90 F_1 score in identifying the VerbNet role. Our study establishes a baseline for Basque SRL. Although there are no directly comparable systems for English we can state that the results we have achieved are quite good. In addition, we have performed a Leave-One-Out feature selection procedure in order to establish which features are the worthiest regarding argument classification. This will help smooth the way for future stages of Basque SRL and will help draw some of the guidelines of our research.
机译:在本文中,我们介绍了为巴斯克开发的第一个语义角色标签系统。该系统是使用机器学习技术实现的,并经过巴斯克处理参考语料库(EPEC)的培训。在我们的实验中,提供最佳结果的分类器基于支持向量机。我们的系统在识别给定成分的PropBank语义角色时达到84.30 F_1评分,在识别VerbNet角色时达到82.90 F_1得分。我们的研究为巴斯克SRL建立了基线。尽管没有直接可比的英语系统,但我们可以说我们所取得的结果是相当不错的。另外,我们已经执行了“留一法”功能选择过程,以便确定哪些功能最适合参数分类。这将有助于为巴斯克SRL的未来发展铺平道路,并有助于得出我们研究的一些指导原则。

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