{"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/a-general-framework-for-information","title":"A General Framework for Information Extraction using Dynamic Span Graphs","arxiv_id":"1904.03296","date":"2019-04-05","proceeding":"NAACL 2019 6","authors":["Yi Luan","Dave Wadden","Luheng He","Amy Shah","Mari Ostendorf","Hannaneh Hajishirzi"],"abstract":"We introduce a general framework for several information extraction tasks\nthat share span representations using dynamically constructed span graphs. The\ngraphs are constructed by selecting the most confident entity spans and linking\nthese nodes with confidence-weighted relation types and coreferences. The\ndynamic span graph allows coreference and relation type confidences to\npropagate through the graph to iteratively refine the span representations.\nThis is unlike previous multi-task frameworks for information extraction in\nwhich the only interaction between tasks is in the shared first-layer LSTM. Our\nframework significantly outperforms the state-of-the-art on multiple\ninformation extraction tasks across multiple datasets reflecting different\ndomains. We further observe that the span enumeration approach is good at\ndetecting nested span entities, with significant F1 score improvement on the\nACE dataset.","url_abs":"http://arxiv.org/abs/1904.03296v1","url_pdf":"http://arxiv.org/pdf/1904.03296v1.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":"a-general-framework-for-information","repo_url":"https://github.com/luanyi/DyGIE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"a-general-framework-for-information","repo_url":"https://github.com/AndrewSukhobok95/DL_GraphEntity_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"a-general-framework-for-information","repo_url":"https://github.com/tricktreat/trimf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"joint-entity-and-relation-extraction","task_name":"Joint Entity and Relation Extraction"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on","task":"Joint Entity and Relation Extraction","dataset":"SciERC","model":"DyGIE","rank_in_archive_order":7,"of":11,"metrics":{"Cross Sentence":"Yes","Entity F1":"65.2","Relation F1":"41.6"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-wlpc","task":"Named Entity Recognition (NER)","dataset":"WLPC","model":"DyGIE","rank_in_archive_order":1,"of":2,"metrics":{"F1":"79.5"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-ace-2004","task":"Relation Extraction","dataset":"ACE 2004","model":"DyGIE","rank_in_archive_order":11,"of":11,"metrics":{"Cross Sentence":"Yes","NER Micro F1":"87.4","RE Micro F1":"59.7"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-ace-2005","task":"Relation Extraction","dataset":"ACE 2005","model":"DyGIE","rank_in_archive_order":10,"of":30,"metrics":{"Cross Sentence":"Yes","NER Micro F1":"88.4","RE Micro F1":"63.2","Sentence Encoder":"ELMo"},"uses_additional_data":true},{"leaderboard":"/sota/relation-extraction-on-wlpc","task":"Relation Extraction","dataset":"WLPC","model":"DyGIE","rank_in_archive_order":2,"of":2,"metrics":{"F1":"64.1"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.03296","atlas_url":"https://app.syntology.ai/?focus=1904.03296","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}