A Preliminary Study on the Robustness and Generalization of Role Sets for Semantic Role Labeling
نویسندگان
چکیده
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-theart SRL system on both role role sets based on the SemEval-2007 English dataset, which comprises the 50 most frequent verbs in PropBank.
منابع مشابه
Robustness and Generalization of Role Sets: PropBank vs. VerbNet
This paper presents an empirical study on the robustness and generalization of two alternative role sets for semantic role labeling: PropBank numbered roles and VerbNet thematic roles. By testing a state–of–the–art SRL system with the two alternative role annotations, we show that the PropBank role set is more robust to the lack of verb–specific semantic information and generalizes better to in...
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