{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unified-structure-generation-for-universal","title":"Unified Structure Generation for Universal Information Extraction","arxiv_id":"2203.12277","date":"2022-03-23","proceeding":"ACL 2022 5","authors":["Yaojie Lu","Qing Liu","Dai Dai","Xinyan Xiao","Hongyu Lin","Xianpei Han","Le Sun","Hua Wu"],"abstract":"Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.","url_abs":"https://arxiv.org/abs/2203.12277v1","url_pdf":"https://arxiv.org/pdf/2203.12277v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unified-structure-generation-for-universal","repo_url":"https://github.com/MS-P3/code5/tree/main/uie","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"unified-structure-generation-for-universal","repo_url":"https://github.com/universal-ie/UIE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"uie","task_name":"UIE"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-aste","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"ASTE","model":"UIE","rank_in_archive_order":5,"of":13,"metrics":{"F1 (L14)":"62.94","F1 (R15)":"64.41","F1 (R16)":"72.86","F1(R14)":"72.55"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.12277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12277"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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