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oai:drops-oai.dagstuhl.de:335 2024-03-06T11:06:18Z ddc:004 open_access

TimeBank-Driven TimeML Analysis Boguraev, Branimir Ando, Rie Kubota TimeML analysis TimeBank corpus TimeML-compliant temporal information extraction finite-state processing machine learning corpus analysis The design of TimeML as an expressive language for temporal information brings promises, and challenges; in particular, its representational properties raise the bar for traditional information extraction methods applied to the task of text-to-TimeML analysis. A reference corpus, such as TimeBank, is an invaluable asset in this situation; however, certain characteristics of TimeBank---size and consistency, primarily---present challenges of their own. We discuss the design, implementation, and performance of an automatic TimeML-compliant annotator, trained on TimeBank, and deploying a hybrid analytical strategy of mixing aggressive finite-state processing over linguistic annotations with a state-of-the-art machine learning technique capable of leveraging large amounts of unannotated data. The results we report are encouraging in the light of a close analysis of TimeBank; at the same time they are indicative of the need for more infrastructure work, especially in the direction of creating a larger and more robust reference corpus. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Branimir Boguraev and Rie Kubota Ando 2005 Is Part Of Dagstuhl Seminar Proceedings, Volume 5151, Annotating, Extracting and Reasoning about Time and Events (2005) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.05151.11 urn:nbn:de:0030-drops-3354 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05151.11 eng https://creativecommons.org/licenses/by/4.0/legalcode