Effective use of TimeBank for TimeML analysis

• Authors: Branimir Boguraev;Rie Kubota Ando
• Venue: Proceedings of the 2005 international conference on Annotating, extracting and reasoning about time and events
• Year: 2005

TimeML is an expressive language for temporal information, but its rich representational properties raise the bar for traditional information extraction methods when applied to the task of text-to-TimeML analysis. We analyse the extent to which timebank, the reference corpus for timeml, supports development of timeml-compliant analytics. The first release of the corpus exhibits challenging characteristics: small size and some noise. Nonetheless, a particular design of a time annotator trained on timebank is able to exploit the data in an implementation which deploys 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. We present our design, in light of encouraging performance results; we also interpret these results in relation to a close analysis of timebank's annotation 'profile'. We conclude that even the first release of the corpus is invaluable; we further argue for more infrastructure work needed to create a larger and more robust reference corpus.1