Automatic labeling of semantic roles
Computational Linguistics
Towards a resource for lexical semantics: a large German corpus with extensive semantic annotation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Support Vector Learning for Semantic Argument Classification
Machine Learning
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Adding predicate argument structure to the Penn TreeBank
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Applying spelling error correction techniques for improving semantic role labelling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Cross-lingual validity of PropBank in the manual annotation of French
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Scaling up automatic cross-lingual semantic role annotation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Towards semi-supervised brazilian portuguese semantic role labeling: building a benchmark
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
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We present an approach to automatic semantic role labeling (SRL) carried out in the context of the Dutch Language Corpus Initiative (D-Coi) project. Adapting earlier research which has mainly focused on English to the Dutch situation poses an interesting challenge especially because there is no semantically annotated Dutch corpus available that can be used as training data. Our automatic SRL approach consists of three steps: bootstrapping from a syntactically annotated corpus by means of a rule-based tagger developed for this purpose, manual correction on the basis of the Prop-Bank guidelines which have been adapted to Dutch and training a machine learning system on the manually corrected data.