Causal Corpus 事件因果关系语料统计

Posted 2022-04-19 gjl-blog

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

名称	年份	规模（因果关系数量）	开源情况	备注
SemEval-2007 task 4	2007	210	是	~
The Penn Discourse Treebank 2.0	2008	~	是	没有专门对因果进行标注。因果被记为 contingency relationship 的子类。显式因果，且触发词不完整，无法完全的表述因果，很多情况没有标记。BECauSE Corpus 2.0相对其更加完善。
Bethard et al., 2008	2008	-	paper中链接以失效	标注了一个小语料库，针对被 ’and‘ 连接的事件binary 因果标注。
SemEval-2010 task 8	2010	1,331	是	每条句子只标注一对因果事件，即使还存在其他因果事件。实体不标注完整信息，只标注head。
Richer Event Descriptions	2014	1,147		对THYME病例语料标注的丰富，添加了事件共指注释，同时实现了相邻句之间的事件关系标注,对因果进行区分， ‘PRECONDITION’ and ‘CAUSE’
Causal-TimeBank	2014	298		提出一种更加广泛覆盖的语言学的方法来丰富 TimeML 语料库，使其包含因果关系和触发词。要求事件是TimeML中标注的事件，基于语言学特征进行标注。guideline 不够精确，更多地依赖于主观概念。
The Chinese Discourse TreeBank	2015	261		找到的唯二中文语料。
CaTeRS	2016	约700	是	320篇小说，1600个句子，2708个事件，2715个关系，13种类型。实体不标注完整信息，只标注head。不是标注现实世界的因果，而是故事中结合人的推理能够得到的因果结论。侧重于script and narrative structure learning
AltLex	2016	9,190		利用PDTB和Wikipedia语料，使用distant supervision demonstrates方法，提出了一种自动构建因果标注集的方法，文末作者提到了他没有对标注的质量进行细致的验证。只是作为一个组件参与分类器从而提升最终性能。
BECauSE Corpus 2.0	2017	1,803	是	显式因果。与其他标注方案的一致性高，语言学因果结构覆盖完整。同时平行标注了其他关系，允许同一事件对包含多种关系。对不同关系间的重叠进行讨论。是目前为止找到的最好的语料。
Event StoryLine Corpus	2017	5,519 PLOT_ LINK	是	该语料对故事进行标注，标注条目PLOT_LINK 表达 explanatory relations ，即说明性的、帮助读者理解故事叙述架构的关系信息，标注结果和因果非常相似，但是出发点又有不同。这种关系的目的是使(新闻)故事中事件的连贯性或逻辑联系变得清晰，为事件之间的一种松散的因果或时序关系，一件事的提及解释了/证明了另一件事的发生。
HIT-CDTB	?	2,138（显式）+1,526（隐式）	否	HIT篇章关系语料。存疑。

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.
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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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