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A Preliminary Study on the Robustness and Generalization of Role Sets for Semantic Role Labeling

机译:语义角色标签中角色集的鲁棒性和泛化初步研究

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Most Semantic Role Labeling (SRL) systems rely on available annotated corpora, being PropBank the most widely used corpus so far. Propbank role set is based on theory-neutral numbered arguments, which are linked to fine grained verb-dependant semantic roles through the verb framesets. Recently, thematic roles from the computational verb lexicon VerbNet have been suggested to be more adequate for generalization and portability of SRL systems, since they represent a compact set of verb-independent general roles widely used in linguistic theory. Such thematic roles could also put SRL systems closer to application needs. This paper presents a comparative study of the behavior of a state-of-the-art SRL system on both role role sets based on the SemEval-2007 English dataset, which comprises the 50 most frequent verbs in PropBank.
机译:大多数语义角色标记(SRL)系统依赖于可用的带注释的语料库,而PropBank是迄今为止使用最广泛的语料库。 Propbank角色集基于与理论无关的编号参数,这些参数通过动词框架集链接到细粒度的依赖于动词的语义角色。最近,已经提出了来自计算动词词典VerbNet的主题角色对于SRL系统的通用性和可移植性更为合适,因为它们代表了一套紧凑的独立于动词的一般角色,广泛用于语言理论。这样的主题角色还可以使SRL系统更接近应用程序需求。本文基于SemEval-2007英语数据集(包括PropBank中的50个最常见动词),对两种角色角色集上的最新SRL系统的行为进行了比较研究。

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