The nature of statistical learning theory
The nature of statistical learning theory
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Evita: a robust event recognizer for QA systems
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A factuality profiler for eventualities in text
A factuality profiler for eventualities in text
Identification of event mentions and their semantic class
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Effective use of TimeBank for TimeML analysis
Proceedings of the 2005 international conference on Annotating, extracting and reasoning about time and events
TimeML events recognition and classification: learning CRF models with semantic roles
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
French TimeBank: an ISO-TimeML annotated reference corpus
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop
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This paper presents an annotation scheme for events in Spanish texts, based on TimeML for English. This scheme is contrasted with different proposals, all of them based on TimeML, for various Romance languages: Italian, French and Spanish. Two manually annotated corpora for Spanish, under the proposed scheme, are now available. While manual annotation is far from trivial, we obtained a very good event identification agreement (93% of events were identically identified by both annotators). Part of the annotated text was used as a training corpus for the automatic recognition of events. In the experiments conducted so far (SVM and CRF) our best results are in the state of the art for this task (80.3% of F-measure).