Summary of the paper

Title TIMEN: An Open Temporal Expression Normalisation Resource
Authors Hector Llorens, Leon Derczynski, Robert Gaizauskas and Estela Saquete
Abstract Temporal expressions are words or phrases that describe a point, duration or recurrence in time. Automatically annotating these expressions is a research goal of increasing interest. Recognising them can be achieved with minimally supervised machine learning, but interpreting them accurately (normalisation) is a complex task requiring human knowledge. In this paper, we present TIMEN, a community-driven tool for temporal expression normalisation. TIMEN is derived from current best approaches and is an independent tool, enabling easy integration in existing systems. We argue that temporal expression normalisation can only be effectively performed with a large knowledge base and set of rules. Our solution is a framework and system with which to capture this knowledge for different languages. Using both existing and newly-annotated data, we present results showing competitive performance and invite the IE community to contribute to a knowledge base in order to solve the temporal expression normalisation problem.
Topics Information Extraction, Information Retrieval, Discourse annotation, representation and processing, Tools, systems, applications
Full paper TIMEN: An Open Temporal Expression Normalisation Resource
Bibtex @InProceedings{LLORENS12.128,
  author = {Hector Llorens and Leon Derczynski and Robert Gaizauskas and Estela Saquete},
  title = {TIMEN: An Open Temporal Expression Normalisation Resource},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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