CATENA: CAusal and TEmporal relation extraction from NAtural language texts

Paramita Mirza, Sara Tonelli


Abstract
We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machine-learned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and TimeBank-Dense data. Although causal relations are much sparser than temporal ones, the architecture and the selected features are mostly suitable to serve both tasks. The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.
Anthology ID:
C16-1007
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
64–75
Language:
URL:
https://aclanthology.org/C16-1007
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/C16-1007.pdf
Code
 paramitamirza/CATENA
Data
TimeBank