UniCausal: Unified Benchmark and Repository for Causal Text Mining-Reference-Cited by-同舟云学术

UniCausal: Unified Benchmark and Repository for Causal Text Mining

Published:2023 Issue: Volume: Page:248-262
ISSN:0302-9743
Container-title:Big Data Analytics and Knowledge Discovery
language:
Short-container-title:

Author:

Tan Fiona Anting^ORCID,Zuo Xinyu,Ng See-Kiong

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-39831-5_23

Reference36 articles.

1. Asghar, N.: Automatic extraction of causal relations from natural language texts: a comprehensive survey. CoRR abs/1605.07895 (2016). http://arxiv.org/abs/1605.07895

2. Ayyanar, R., Koomullil, G., Ramasangu, H.: Causal relation classification using convolutional neural networks and grammar tags. In: 2019 IEEE 16th India Council International Conference (INDICON), pp. 1–3 (2019). https://doi.org/10.1109/INDICON47234.2019.9028985

3. Cao, P., et al.: Knowledge-enriched event causality identification via latent structure induction networks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4862–4872. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.376. https://aclanthology.org/2021.acl-long.376

4. Caselli, T., Vossen, P.: The event StoryLine corpus: a new benchmark for causal and temporal relation extraction. In: Proceedings of the Events and Stories in the News Workshop, Vancouver, Canada, pp. 77–86. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/W17-2711. https://aclanthology.org/W17-2711

5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/N19-1423. https://aclanthology.org/N19-1423