TimeBank-Driven TimeML Analysis

Abstract

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.