# Persian Causality Corpus (PerCause) and the Causality Detection Benchmark

arXiv: https://arxiv.org/abs/2106.14165 · HTML: https://mlsanity.com/paper/2106.14165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Extracting Temporal and Causal Relations between Events](https://mlsanity.com/paper/1604.08120/) (2016)
- [A corpus of preposition supersenses in English web reviews](https://mlsanity.com/paper/1605.02257/) (2016)

## Abstract (arXiv)

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