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CATENA: CAusal and TEmporal relation extraction from NAtural language texts
International Conference on Computational Linguistics
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 machinelearned 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 anddblp:conf/coling/MirzaT16 fatcat:qxlrgypnr5d7hazgy6ykakqmba