Weakly Supervised Multilingual Causality Extraction from Wikipedia

Chikara Hashimoto


Abstract
We present a method for extracting causality knowledge from Wikipedia, such as Protectionism -> Trade war, where the cause and effect entities correspond to Wikipedia articles. Such causality knowledge is easy to verify by reading corresponding Wikipedia articles, to translate to multiple languages through Wikidata, and to connect to knowledge bases derived from Wikipedia. Our method exploits Wikipedia article sections that describe causality and the redundancy stemming from the multilinguality of Wikipedia. Experiments showed that our method achieved precision and recall above 98% and 64%, respectively. In particular, it could extract causalities whose cause and effect were written distantly in a Wikipedia article. We have released the code and data for further research.
Anthology ID:
D19-1296
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2988–2999
Language:
URL:
https://aclanthology.org/D19-1296
DOI:
10.18653/v1/D19-1296
Bibkey:
Cite (ACL):
Chikara Hashimoto. 2019. Weakly Supervised Multilingual Causality Extraction from Wikipedia. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2988–2999, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Weakly Supervised Multilingual Causality Extraction from Wikipedia (Hashimoto, EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1296.pdf