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Recognizing causal elements and causal relations in text is one of thechallenging issues in natural language processing; specifically, in lowresource languages such as Persian. In this research we prepare a causalityhuman annotated corpus for the Persian language which consists of 4446sentences and 5128 causal relations and three labels of cause, effect andcausal mark -- if possibl -- are specified for each relation. We have used thiscorpus to train a system for detecting causal elements boundaries. Also, wepresent a causality detection benchmark for three machine learning methods andtwo deep learning systems based on this corpus. Performance evaluationsindicate that our best total result is obtained through CRF classifier whichhas F-measure of 0.76 and the best accuracy obtained through Bi-LSTM-CRF deeplearning method with Accuracy equal to %91.4.

Persian Causality Corpus (PerCause) and the Causality Detection Benchmark