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.
Cite
CITATION STYLE
Hashimoto, C. (2019). Weakly supervised multilingual causality extraction from wikipedia. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference (pp. 2988–2999). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1296
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