CATENA is 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. The system requires pre-annotated text with EVENT and TIMEX3 tags according to the TimeML annotation standard, as these annotation are used as features to extract the relations.
CATENA contains two main modules:
- Temporal module, a combination of rule-based and supervised classifiers, with a temporal reasoner module in between.
- Causal module, a combination of a rule-based classifier according to causal verbs, and supervised classifier taken into account syntactic and context features, especially causal signals appearing in the text.
The two modules interact, based on the assumption that the notion of causality is tightly connected with the temporal dimension: (i) TLINK labels for event-event pairs, resulting from the rule-based sieve + temporal reasoner, are used for the CLINK classifier, and (ii) CLINK labels are used as a post-editing method for correcting the wrongly labelled event pairs by the Temporal module.
The software and further information on how to install and use it are available at this link: https://github.com/dhfbk/CATENA
Paramita Mirza and Sara Tonelli. 2016. CATENA: CAusal and TEmporal relation extraction from NAtural language texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, December. [bib] [pdf]