{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:00:43Z","timestamp":1768341643157,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T00:00:00Z","timestamp":1659225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2020AAA0109300"],"award-info":[{"award-number":["2020AAA0109300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>At present, there is no uniform definition of annotation schemes for causal extraction, and existing methods are limited by the dependence of relations on long spans, which makes complex sentences such as multi-causal relations and nested causal relations difficult to extract. To solve these problems, a head-to-tail entity annotation method is proposed, which can express the complete semantics of complex causal relations and clearly describe the boundaries of entities. Based on this, a causal model, RPA-GCN (relation position and attention-graph convolutional networks), is constructed, incorporating GAT (graph attention network) and entity location perception. The attention layer is combined with a dependency tree to enhance the model\u2019s ability to perceive relational features, and a bi-directional graph convolutional network is constructed to further capture the deep interaction information between entities and relationships. Finally, the classifier iteratively predicts the relationship of each word pair in the sentence and analyzes all causal pairs in the sentence by a scoring function. Experiments on SemEval 2010 task 8 and the Altlex dataset show that our proposed method has significant advantages in solving complex causal extraction compared to state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/info13080364","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T02:06:42Z","timestamp":1659319602000},"page":"364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Yang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weibing","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5476-620X","authenticated-orcid":false,"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1145\/3241036","article-title":"The seven tools of causal inference, with reflections on machine learning","volume":"62","author":"Pearl","year":"2019","journal-title":"Commun. 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