Knowledge Commons of Institute of Automation,CAS
Local-to-Global Causal Reasoning for Cross-Document Relation Extraction | |
Haoran Wu![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE/CAA Journal of Automatica Sinica
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ISSN | 2329-9266 |
2023 | |
卷号 | 10期号:7页码:1608-1621 |
摘要 | Cross-document relation extraction (RE), as an extension of information extraction, requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts. Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity. However, similarity-based methods cannot distinguish valid information from highly similar retrieved documents well. How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue. To address this problem, we design a novel local-to-global causal reasoning (LGCR) network for cross-document RE, which enables efficient distinguishing, filtering and global reasoning on complex information from a causal perspective. Specifically, we propose a local causal estimation algorithm to estimate the causal effect, which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE. Furthermore, based on the causal effect, we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning. Experimental results under the closed and the open settings of the large-scale dataset CodRED demonstrate our LGCR network significantly outperforms the state-of-the-art methods and validate the effectiveness of causal reasoning in confusing information processing. |
关键词 | Causal reasoning cross document graph reasoning relation extraction (RE) |
DOI | 10.1109/JAS.2023.123540 |
WOS记录号 | WOS:001012817900008 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51997 |
专题 | 学术期刊_IEEE/CAA Journal of Automatica Sinica |
推荐引用方式 GB/T 7714 | Haoran Wu,Xiuyi Chen,Zefa Hu,et al. Local-to-Global Causal Reasoning for Cross-Document Relation Extraction[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(7):1608-1621. |
APA | Haoran Wu,Xiuyi Chen,Zefa Hu,Jing Shi,Shuang Xu,&Bo Xu.(2023).Local-to-Global Causal Reasoning for Cross-Document Relation Extraction.IEEE/CAA Journal of Automatica Sinica,10(7),1608-1621. |
MLA | Haoran Wu,et al."Local-to-Global Causal Reasoning for Cross-Document Relation Extraction".IEEE/CAA Journal of Automatica Sinica 10.7(2023):1608-1621. |
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