Representation and Learning of Temporal Relations

Leon Derczynski


Abstract
Determining the relative order of events and times described in text is an important problem in natural language processing. It is also a difficult one: general state-of-the-art performance has been stuck at a relatively low ceiling for years. We investigate the representation of temporal relations, and empirically evaluate the effect that various temporal relation representations have on machine learning performance. While machine learning performance decreases with increased representational expressiveness, not all representation simplifications have equal impact.
Anthology ID:
C16-1182
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1937–1948
Language:
URL:
https://aclanthology.org/C16-1182
DOI:
Bibkey:
Cite (ACL):
Leon Derczynski. 2016. Representation and Learning of Temporal Relations. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1937–1948, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Representation and Learning of Temporal Relations (Derczynski, COLING 2016)
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PDF:
https://aclanthology.org/C16-1182.pdf