Jiaoyun Yang
ABSTRACT
Extracting causality from literature has become an important task due to the essential role of causality. Traditional methods use pattern matching to extract causality, requiring domain knowledge and extensive human effort. Recent researches focus on utilizing pre-trained language models due to their success in Natural Language Processing (NLP). However, long sentences in literature hinders the performance of causality extraction. In this paper, we propose to focus on the representation of causal virtual pattern <head_entity, causal_virtual_trigger, tail_entity> and design a Causal Pattern Representation Learning (CPRL) method to tackle this challenge. For the causal_virtual_trigger representation, CPRL applies the attention mechanism on the shortest dependency path between entities to filter irrelevant information. For the head_entity and tail_entity representation, CPRL applies graph convolution networks to encode word dependency on entities. By crawling health-related literature abstracts, we create a new causality extraction dataset, namely HealthCE, with a size of 3479. Experiments on HealthCE demonstrate the effectiveness of our approach over existing causality extraction and general relation extraction baselines on the task of causality extraction.
- Ning An, Yongbo Xiao, Jing Yuan, Jiaoyun Yang, and Gil Alterovitz. 2019. Extracting causal relations from the literature with word vector mapping. Computers in biology and medicine 115 (2019), 103524.Google Scholar
- Eduardo Blanco, Nuria Castell, and Dan Moldovan. 2008. Causal relation extraction. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08).Google Scholar
- Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2022. Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction. In Proceedings of the ACM Web Conference 2022. 2778–2788.Google ScholarDigital Library
- Tharini N De Silva, Xiao Zhibo, Zhao Rui, and Mao Kezhi. 2017. Causal relation identification using convolutional neural networks and knowledge based features. International Journal of Computer and Systems Engineering 11, 6(2017), 696–701.Google Scholar
- Roxana Girju, Dan I Moldovan, 2002. Text mining for causal relations.. In FLAIRS conference. 360–364.Google Scholar
- Siyi Guo, Liuqi Jin, Jiaoyun Yang, Mengyao Jiang, Lin Han, and Ning An. 2020. Causal extraction from the literature of pressure injury and risk factors. In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 581–585.Google ScholarCross Ref
- Vivek Khetan, Roshni Ramnani, Mayuresh Anand, Subhashis Sengupta, and Andrew E Fano. 2022. Causal BERT: Language models for causality detection between events expressed in text. In Intelligent Computing. Springer, 965–980.Google Scholar
- Manolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev, 2019. Transfer learning for causal sentence detection. arXiv preprint arXiv:1906.07544(2019).Google Scholar
- Cheng Li and Ye Tian. 2020. Downstream model design of pre-trained language model for relation extraction task. arXiv preprint arXiv:2004.03786(2020).Google Scholar
- Pengfei Li and Kezhi Mao. 2019. Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts. Expert Systems with Applications 115 (2019), 512–523.Google ScholarCross Ref
- Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025(2015).Google Scholar
- Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. 55–60.Google ScholarCross Ref
- Yatian Shen and Xuan-Jing Huang. 2016. Attention-based convolutional neural network for semantic relation extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2526–2536.Google Scholar
- Qiongxing Tao, Xiangfeng Luo, Hao Wang, and Richard Xu. 2019. Enhancing relation extraction using syntactic indicators and sentential contexts. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 1574–1580.Google ScholarCross Ref
- Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861–6871.Google Scholar
- Shanchan Wu and Yifan He. 2019. Enriching pre-trained language model with entity information for relation classification. In Proceedings of the 28th ACM international conference on information and knowledge management. 2361–2364.Google ScholarDigital Library
- Yuhao Zhang, Peng Qi, and Christopher D Manning. 2018. Graph convolution over pruned dependency trees improves relation extraction. arXiv preprint arXiv:1809.10185(2018).Google Scholar
- Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. 2016. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers). 207–212.Google ScholarCross Ref
Index Terms
- Causal Pattern Representation Learning for Extracting Causality from Literature
Recommendations
Inter-sentence and Implicit Causality Extraction from Chinese Corpus
Advances in Knowledge Discovery and Data MiningAbstractAutomatically extracting causal relations from texts is a challenging task in Natural Language Processing (NLP). Most existing methods focus on extracting intra-sentence or explicit causality, while neglecting the causal relations that expressed ...
Building a Corpus for Chinese Causality Extraction in Futures Domain
Chinese Lexical SemanticsAbstractCausality extraction is to extract cause events and effect events from text, which is the basis for constructing causal event graphs and doing causal reasoning. The research on causality extraction needs the support of annotated causality corpus. ...
Nested Causality Extraction on Traffic Accident Texts as Question Answering
Natural Language Processing and Chinese ComputingAbstractAs an important type of relationship, causality plays a vital role in relation reasoning. Therefore, causality extraction from natural language texts is a crucial task, especially in the field of traffic. For example, we can quickly discover the ...
Comments