2005/01/01 by Branimir Boguraev, Boguraev, Branimir, Rie Kubota Ando +1 · 1 citation
Computer Science · #Natural Language Processing Techniques #Semantic Web and Ontologies #TimeBank corpus #TimeML analysis #TimeML-compliant temporal information extraction #Topic Modeling #corpus analysis #finite-state processing #machine learning
paper · doi:10.4230/dagsemproc.05151.11
openalex publication_date 2005/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.