@inproceedings{hashimoto-2019-weakly,
title = "Weakly Supervised Multilingual Causality Extraction from {W}ikipedia",
author = "Hashimoto, Chikara",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1296",
doi = "10.18653/v1/D19-1296",
pages = "2988--2999",
abstract = "We present a method for extracting causality knowledge from Wikipedia, such as Protectionism -{\textgreater} 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.",
}
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%0 Conference Proceedings
%T Weakly Supervised Multilingual Causality Extraction from Wikipedia
%A Hashimoto, Chikara
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hashimoto-2019-weakly
%X We present a method for extracting causality knowledge from Wikipedia, such as Protectionism -\textgreater 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.
%R 10.18653/v1/D19-1296
%U https://aclanthology.org/D19-1296
%U https://doi.org/10.18653/v1/D19-1296
%P 2988-2999
Markdown (Informal)
[Weakly Supervised Multilingual Causality Extraction from Wikipedia](https://aclanthology.org/D19-1296) (Hashimoto, EMNLP-IJCNLP 2019)
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.