{"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/flert-document-level-features-for-named","title":"FLERT: Document-Level Features for Named Entity Recognition","arxiv_id":"2011.06993","date":"2020-11-13","proceeding":null,"authors":["Stefan Schweter","Alan Akbik"],"abstract":"Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features. In this paper, we perform a comparative evaluation of document-level features in the two standard NER architectures commonly considered in the literature, namely \"fine-tuning\" and \"feature-based LSTM-CRF\". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality. We present experiments from which we derive recommendations for how to model document context and present new state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.","url_abs":"https://arxiv.org/abs/2011.06993v2","url_pdf":"https://arxiv.org/pdf/2011.06993v2.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":"flert-document-level-features-for-named","repo_url":"https://github.com/flairNLP/flair","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"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":"sentence","task_name":"Sentence"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-on-conll-2002-dutch","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2002 (Dutch)","model":"FLERT XLM-R","rank_in_archive_order":2,"of":6,"metrics":{"F1":"95.21"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-conll-2002","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2002 (Spanish)","model":"FLERT XLM-R","rank_in_archive_order":4,"of":6,"metrics":{"F1":"90.14"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-ner-on-conll-2003","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (English)","model":"FLERT XLM-R","rank_in_archive_order":6,"of":73,"metrics":{"F1":"94.09"},"uses_additional_data":true},{"leaderboard":"/sota/named-entity-recognition-on-conll-2003-german","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (German)","model":"FLERT XLM-R","rank_in_archive_order":2,"of":6,"metrics":{"F1":"88.34"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-conll-2003-german-1","task":"Named Entity Recognition (NER)","dataset":"CoNLL 2003 (German) Revised","model":"FLERT XLM-R","rank_in_archive_order":1,"of":5,"metrics":{"F1":"92.23"},"uses_additional_data":false},{"leaderboard":"/sota/named-entity-recognition-on-findvehicle","task":"Named Entity Recognition (NER)","dataset":"FindVehicle","model":"FLERT","rank_in_archive_order":2,"of":3,"metrics":{"F1 Score":"80.9"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2011.06993","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}