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We propose a new approach to characterizing the timeline of a text: temporal dependency structures, where all the events of a narrative are linked via partial ordering relations like BE FORE , AFTER , OVERLAP and IDENTITY . We annotate a corpus of children’s stories with temporal dependency trees, achieving agreement (Krippendorff’s Alpha) of 0.856 on the event words, 0.822 on the links between events, and of 0.700 on the ordering relation labels. | Extracting Narrative Timelines as Temporal Dependency Structures Oleksandr Kolomiyets KU Leuven Celestijnenlaan 200A B-3001 Heverlee Belgium Oleksandr.Kolomiyets@ cs.kuleuven.be Steven Bethard University of Colorado Campus Box 594 Boulder CO 80309 USA Steven.Bethard@ colorado.edu Marie-Francine Moens KU Leuven Celestijnenlaan 200A B-3001 Heverlee Belgium Sien.Moens@ cs.kuleuven.be Abstract We propose a new approach to characterizing the timeline of a text temporal dependency structures where all the events of a narrative are linked via partial ordering relations like BEFORE AFTER OVERLAP and IDENTITY. We annotate a corpus of children s stories with temporal dependency trees achieving agreement Krippendorff s Alpha of 0.856 on the event words 0.822 on the links between events and of 0.700 on the ordering relation labels. We compare two parsing models for temporal dependency structures and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596. Our analysis of the dependency parser errors gives some insights into future research directions. 1 Introduction There has been much recent interest in identifying events times and their relations along the timeline from event and time ordering problems in the TempEval shared tasks Verhagen et al. 2007 Verhagen et al. 2010 to identifying time arguments of event structures in the Automated Content Extraction program Linguistic Data Consortium 2005 Gupta and Ji 2009 to timestamping event intervals in the Knowledge Base Population shared task Artiles et al. 2011 Amigo etal. 2011 . However to date this research has produced fragmented document timelines because only specific types of temporal relations in specific contexts have 88 been targeted. For example the TempEval tasks only looked at relations between events in the same or adjacent sentences Verhagen et al. 2007 Verhagen et al. .
