tailieunhanh - Báo cáo khoa học: "Scaling up Automatic Cross-Lingual Semantic Role Annotation"

Broad-coverage semantic annotations for training statistical learners are only available for a handful of languages. Previous approaches to cross-lingual transfer of semantic annotations have addressed this problem with encouraging results on a small scale. In this paper, we scale up previous efforts by using an automatic approach to semantic annotation that does not rely on a semantic ontology for the target language. | Scaling up Automatic Cross-Lingual Semantic Role Annotation Lonneke van der Plas Paola Merlo James Henderson Department of Linguistics Department of Linguistics Department of Computer Science University of Geneva University of Geneva University of Geneva Geneva Switzerland Geneva Switzerland Geneva Switzerland @ Abstract Broad-coverage semantic annotations for training statistical learners are only available for a handful of languages. Previous approaches to cross-lingual transfer of semantic annotations have addressed this problem with encouraging results on a small scale. In this paper we scale up previous efforts by using an automatic approach to semantic annotation that does not rely on a semantic ontology for the target language. Moreover we improve the quality of the transferred semantic annotations by using a joint syntactic-semantic parser that learns the correlations between syntax and semantics of the target language and smooths out the errors from automatic transfer. We reach a labelled F-measure for predicates and arguments of only 4 and 9 points respectively lower than the upper bound from manual annotations. 1 Introduction As data-driven techniques tackle more and more complex natural language processing tasks it becomes increasingly unfeasible to use complete accurate hand-annotated data on a large scale for training models in all languages. One approach to addressing this problem is to develop methods that automatically generate annotated data by transferring annotations in parallel corpora from languages for which this information is available to languages for which these data are not available Yarowsky et al. 2001 Fung et al. 2007 Pado and Lapata 2009 . Previous work on the cross-lingual transfer of semantic annotations Pado 2007 Basili et al. 2009 299 has produced annotations of good quality for test sets that were carefully selected based on semantic ontologies on the source and target side.