Modeling and Learning Vague Event Durations for Temporal Reasoning
Abstract
This paper reports on our recent work on modeling and automatically extracting vague, implicit event durations from text (Pan et al., 2006a, 2006b). It is a kind of commonsense knowledge that can have a substantial impact on temporal reasoning problems. We have also proposed a method of using normal distributions to model judgments that are intervals on a scale and measure their inter-annotator agreement; this should extend from time to other kinds of vague but substantive information in text and commonsense reasoning.
Cite
Text
Pan et al. "Modeling and Learning Vague Event Durations for Temporal Reasoning." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Pan et al. "Modeling and Learning Vague Event Durations for Temporal Reasoning." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/pan2007aaai-modeling/)BibTeX
@inproceedings{pan2007aaai-modeling,
title = {{Modeling and Learning Vague Event Durations for Temporal Reasoning}},
author = {Pan, Feng and Mulkar, Rutu and Hobbs, Jerry R.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {1659-1662},
url = {https://mlanthology.org/aaai/2007/pan2007aaai-modeling/}
}