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Causal Corpus 事件因果关系语料统计

本文是对因果关系抽取领域数据库标注及开源情况的统计。除了对因果关系的标注，一些类似的语料也包含在内，从而为语料的使用提供灵活性，可以根据不同的目标选取不同的语料库。

领域简介

因果关系通常标注为 ( cause , effect , signal ) 三元组，cause 和 effect 分别代表原因事件和结果事件，signal 是语言学从因果结构的触发词，例如 because, so, thus 等等。

需要知道的是不同的因果语料对于因果关系的定义以及对事件的定义有很大差异，从而导致至今没有一个大规模的统一语料库支撑该领域开展开放域的研究。如果给出好的定义也是学术界讨论的焦点。

因果事件语料通常作为因果事件抽取、因果推断等任务的基础，允许使用规则、机器学习、深度学习等方法对事件链进行分析。

采样策略

本文采用的语料搜集方法是基于领域关键词（如 causal, relation, causality ）从 Google Scholar 获取种子论文集，根据文献之间的引述关系，不断拓展相关文档范围，最终得到领域相关的语料集合。

对于 arxiv 暂不收录，只针对已发表的文章进行统计。

统计分析

对于各个语料的具体分析尚未整理完善，有需要的看官可以邮件联系我。

参考资料

1. Girju R, Nakov P, Nastase V, et al. Semeval-2007 task 04: Classification of semantic relations between nominals[C]//Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics, 2007: 13-18.
2. Prasad R, Dinesh N, Lee A, et al. The Penn Discourse TreeBank 2.0[C]//LREC. 2008.
3. Bethard S, Corvey W J, Klingenstein S, et al. Building a Corpus of Temporal-Causal Structure[C]//LREC. 2008.
4. Hendrickx I, Kim S N, Kozareva Z, et al. Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals[C]//Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Association for Computational Linguistics, 2009: 94-99.
5. O’Gorman T, Wright-Bettner K, Palmer M. Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation[C]//Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016). 2016: 47-56.
6. Mirza P, Sprugnoli R, Tonelli S, et al. Annotating causality in the TempEval-3 corpus[C]//EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL). Association for Computational Linguistics, 2014: 10-19.
7. Zhou Y, Xue N. The Chinese Discourse TreeBank: a Chinese corpus annotated with discourse relations[J]. Language Resources and Evaluation, 2015, 49(2): 397-431.
8. Mostafazadeh N, Grealish A, Chambers N, et al. CaTeRS: Causal and temporal relation scheme for semantic annotation of event structures[C]//Proceedings of the Fourth Workshop on Events. 2016: 51-61.
9. Hidey C, McKeown K. Identifying causal relations using parallel Wikipedia articles[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016: 1424-1433.
10. Dunietz J, Levin L, Carbonell J. The BECauSE corpus 2.0: Annotating causality and overlapping relations[C]//Proceedings of the 11th Linguistic Annotation Workshop. 2017: 95-104.
11. Caselli T, Vossen P. The event storyline corpus: A new benchmark for causal and temporal relation extraction[C]//Proceedings of the Events and Stories in the News Workshop. 2017: 77-86.
12. T. N. de Silva, X. Zhibo, Z. Rui, M. Kezhi, Causal relation identification using convolutional neural networks and knowledge based features, World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 11 (6) (2017) 697–702.
13. C. Kruengkrai, K. Torisawa, C. Hashimoto, J. Kloetzer, J. Oh, M. Tanaka, Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., 2017, pp. 3466–3473.
14. C. Kruengkrai, K. Torisawa, C. Hashimoto, J. Kloetzer, J. Oh, M. Tanaka, Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., 2017, pp. 3466–3473.
15. C. Kruengkrai, K. Torisawa, C. Hashimoto, J. Kloetzer, J. Oh, M. Tanaka, Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., 2017, pp. 3466–3473.
16. C. Kruengkrai, K. Torisawa, C. Hashimoto, J. Kloetzer, J. Oh, M. Tanaka, Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks, in: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., 2017, pp. 3466–3473.
17. J. Dunietz, J. G. Carbonell, L. S. Levin, Deepcx: A transition-based approach for shallow semantic parsing with complex constructional triggers, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, 2018, pp. 1691–1701.

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Causal Corpus 事件因果关系语料统计

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原文地址：https://www.cnblogs.com/gjl-blog/p/11197939.html