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For computational accuracy concerns or other reasons, traditional works often do entity recognition and relation extraction separately; it might put less attention over the task connections and triplet global association. We propose the Global Table Attention GRU (GL-TGRU) model as a joint approach that uses sequence information encoding and table information encoding to jointly learn the representation and enhance the global association of entity and relation in table filling. We evaluated the proposed model on the public data set SciERC and the natural disaster data set SSD-HDS, respectively. The\n                    <jats:italic>F<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    scores of experimental results of the GL-TGRU model for entity identification and relation extraction achieved 65.81% and 37.30% on SciERC and achieved 93.89% and 84.06% on SSD-HDS. The results show our model helps to capture more global association relations of entity and relation, which can better identify the entity\u2013relation triplet information.\n                  <\/jats:p>","DOI":"10.1177\/01655515221144862","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T07:59:40Z","timestamp":1673510380000},"page":"797-812","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Joint entity\u2013relation extraction for natural disaster based on table filling"],"prefix":"10.1177","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2369-7093","authenticated-orcid":false,"given":"Cheng","family":"Cheng","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingtian","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shansong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"7999","volume-title":"Proceedings of the 58th annual meeting of the association for computational linguistics","author":"Lin Y","unstructured":"Lin Y, Ji H, Huang F et al. 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