S. Fissaha Adafre
M. de Rijke
- Towards Task-Based Temporal Extraction and Recognition
- Book/source title
- Proceedings Dagstuhl Workshop on Annotating, Extracting, and Reasoning about Time and Events
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
We seek to improve the robustness and portability of temporal information extraction systems by incorporating data-driven techniques. We present two sets of experiments pointing us in this direction. The first shows that machine-learning-based recognition of temporal expressions
not only achieves high accuracy on its own but can also improve rule-based normalization. The second makes use of a staged normalization architecture to experiment with machine learned classifiers for certain disambiguation sub-tasks within the normalization task.
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