{"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/parallel-instance-query-network-for-named","title":"Parallel Instance Query Network for Named Entity Recognition","arxiv_id":"2203.10545","date":"2022-03-20","proceeding":"ACL 2022 5","authors":["Yongliang Shen","Xiaobin Wang","Zeqi Tan","Guangwei Xu","Pengjun Xie","Fei Huang","Weiming Lu","Yueting Zhuang"],"abstract":"Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one type of entities per inference, which is inefficient. Second, the extraction for different types of entities is isolated, ignoring the dependencies between them. Third, query construction relies on external knowledge and is difficult to apply to realistic scenarios with hundreds of entity types. To deal with them, we propose Parallel Instance Query Network (PIQN), which sets up global and learnable instance queries to extract entities from a sentence in a parallel manner. Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel. Instead of being constructed from external knowledge, instance queries can learn their different query semantics during training. For training the model, we treat label assignment as a one-to-many Linear Assignment Problem (LAP) and dynamically assign gold entities to instance queries with minimal assignment cost. Experiments on both nested and flat NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.","url_abs":"https://arxiv.org/abs/2203.10545v1","url_pdf":"https://arxiv.org/pdf/2203.10545v1.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":"parallel-instance-query-network-for-named","repo_url":"https://github.com/tricktreat/piqn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"chinese-named-entity-recognition","task_name":"Chinese Named Entity Recognition"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"nested-named-entity-recognition","task_name":"Nested Named Entity Recognition"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/chinese-named-entity-recognition-on-msra","task":"Chinese Named Entity Recognition","dataset":"MSRA","model":"PIQN","rank_in_archive_order":17,"of":21,"metrics":{"F1":"93.48"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-ace-2004","task":"Named Entity Recognition (NER)","dataset":"ACE 2004","model":"PIQN","rank_in_archive_order":2,"of":9,"metrics":{"F1":"88.14","Multi-Task Supervision":"n"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-ace-2005","task":"Named Entity Recognition (NER)","dataset":"ACE 2005","model":"PIQN","rank_in_archive_order":4,"of":20,"metrics":{"F1":"87.42"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"PIQN","rank_in_archive_order":35,"of":73,"metrics":{"F1":"92.87"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-few-nerd-sup","task":"Named Entity Recognition (NER)","dataset":"Few-NERD (SUP)","model":"PIQN","rank_in_archive_order":2,"of":6,"metrics":{"F1-Measure":"69.67","Precision":"70.16","Recall":"69.18"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-few-nerd-sup","task":"Named Entity Recognition (NER)","dataset":"Few-NERD (SUP)","model":"Sequence-to-Set","rank_in_archive_order":4,"of":6,"metrics":{"F1-Measure":"68.23","Precision":"67.37","Recall":"69.12"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-few-nerd-sup","task":"Named Entity Recognition (NER)","dataset":"Few-NERD (SUP)","model":"Locate and Label","rank_in_archive_order":5,"of":6,"metrics":{"F1-Measure":"67.64","Precision":"64.69","Recall":"70.87"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-ontonotes-v5","task":"Named Entity Recognition (NER)","dataset":"Ontonotes v5 (English)","model":"PIQN","rank_in_archive_order":6,"of":28,"metrics":{"F1":"90.96"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-ace-2004","task":"Nested Named Entity Recognition","dataset":"ACE 2004","model":"PIQN","rank_in_archive_order":6,"of":24,"metrics":{"F1":"88.14"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-ace-2005","task":"Nested Named Entity Recognition","dataset":"ACE 2005","model":"PIQN","rank_in_archive_order":3,"of":25,"metrics":{"F1":"87.42"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-genia","task":"Nested Named Entity Recognition","dataset":"GENIA","model":"PIQN","rank_in_archive_order":1,"of":26,"metrics":{"F1":"81.77"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-nne","task":"Nested Named Entity Recognition","dataset":"NNE","model":"PIQN","rank_in_archive_order":3,"of":6,"metrics":{"Micro F1":"94.04"},"uses_additional_data":false},{"leaderboard":"/sota/nested-named-entity-recognition-on-tac-kbp","task":"Nested Named Entity Recognition","dataset":"TAC-KBP 2017","model":"PIQN","rank_in_archive_order":3,"of":3,"metrics":{"F1":"84.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.10545","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}