{"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/named-entity-recognition-in-the-romanian","title":"Named Entity Recognition in the Romanian Legal Domain","arxiv_id":null,"date":"2021-11-01","proceeding":"EMNLP (NLLP) 2021 11","authors":["Vasile Pais","Maria Mitrofan","Carol Luca Gasan","Vlad Coneschi","Alexandru Ianov"],"abstract":"Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian legal domain. The system makes use of the gold annotated LegalNERo corpus. Furthermore, the system combines multiple distributional representations of words, including word embeddings trained on a large legal domain corpus. All the resources, including the corpus, model and word embeddings are open sourced. Finally, the best system is available for direct usage in the RELATE platform.","url_abs":"https://aclanthology.org/2021.nllp-1.2","url_pdf":"https://aclanthology.org/2021.nllp-1.2.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":"named-entity-recognition-in-the-romanian","repo_url":"https://github.com/racai-ai/LegalNER","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"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":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[{"slug":"legalnero","name":"LegalNERo","full_name":"Romanian Named Entity Recognition in the Legal domain"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/named-entity-recognition-on-legalnero","task":"Named Entity Recognition (NER)","dataset":"LegalNERo","model":"Marcell","rank_in_archive_order":1,"of":1,"metrics":{"Avg F1":"85.34"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}