{"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/pack-together-entity-and-relation-extraction","title":"Packed Levitated Marker for Entity and Relation Extraction","arxiv_id":"2109.06067","date":"2021-09-13","proceeding":"ACL 2022 5","authors":["Deming Ye","Yankai Lin","Peng Li","Maosong Sun"],"abstract":"Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder. In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information. Furthermore, for those more complicated span pair classification tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects to model the interrelation between the same-subject span pairs. The experimental results show that, with the enhanced marker feature, our model advances baselines on six NER benchmarks, and obtains a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models on ACE04 and ACE05.","url_abs":"https://arxiv.org/abs/2109.06067v5","url_pdf":"https://arxiv.org/pdf/2109.06067v5.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":"pack-together-entity-and-relation-extraction","repo_url":"https://github.com/thunlp/pl-marker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pack-together-entity-and-relation-extraction","repo_url":"https://github.com/tomaarsen/spanmarkerner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"packed-levitated-markers","method_name":"Packed Levitated Markers"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[{"slug":"packed-levitated-markers","name":"Packed Levitated Markers","full_name":"Packed Levitated Markers"}],"results":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on","task":"Joint Entity and Relation Extraction","dataset":"SciERC","model":"PL-Marker","rank_in_archive_order":1,"of":11,"metrics":{"Cross Sentence":"Yes","Entity F1":"69.9","RE+ Micro F1":"41.6","Relation F1":"53.2"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"PL-Marker","rank_in_archive_order":7,"of":73,"metrics":{"F1":"94.0"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-few-nerd-sup","task":"Named Entity Recognition (NER)","dataset":"Few-NERD (SUP)","model":"PL-Marker","rank_in_archive_order":1,"of":6,"metrics":{"F1-Measure":"70.9","Precision":"71.2","Recall":"70.6"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-ontonotes-v5","task":"Named Entity Recognition (NER)","dataset":"Ontonotes v5 (English)","model":"PL-Marker","rank_in_archive_order":2,"of":28,"metrics":{"F1":"91.9","Precision":"92.0","Recall":"91.7"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-ace-2004","task":"Relation Extraction","dataset":"ACE 2004","model":"PL-Marker","rank_in_archive_order":1,"of":11,"metrics":{"Cross Sentence":"Yes","NER Micro F1":"90.4","RE Micro F1":"69.7","RE+ Micro F1":"66.5"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-ace-2005","task":"Relation Extraction","dataset":"ACE 2005","model":"PL-Marker","rank_in_archive_order":2,"of":30,"metrics":{"Cross Sentence":"Yes","NER Micro F1":"91.1","RE Micro F1":"73.0","RE+ Micro F1":"71.1","Sentence Encoder":"ALBERT"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.06067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.06067"}},"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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