{"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/dadmatools-natural-language-processing","title":"DadmaTools: Natural Language Processing Toolkit for Persian Language","arxiv_id":null,"date":"2022-07-01","proceeding":"NAACL (ACL) 2022 7","authors":["Romina Etezadi","Mohammad Karrabi","Najmeh Zare","Mohamad Bagher Sajadi","Mohammad Taher Pilehvar"],"abstract":"We introduce DadmaTools, an open-source Python Natural Language Processing toolkit for the Persian language. The toolkit is a neural pipeline based on spaCy for several text processing tasks, including normalization, tokenization, lemmatization, part-of-speech, dependency parsing, constituency parsing, chunking, and ezafe detecting. DadmaTools relies on fine-tuning of ParsBERT using the PerDT dataset for most of the tasks. Dataset module and embedding module are included in DadmaTools that support different Persian datasets, embeddings, and commonly used functions for them. Our evaluations show that DadmaTools can attain state-of-the-art performance on multiple NLP tasks. The source code is freely available at https://github.com/Dadmatech/DadmaTools.","url_abs":"https://aclanthology.org/2022.naacl-demo.13","url_pdf":"https://aclanthology.org/2022.naacl-demo.13.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":"dadmatools-natural-language-processing","repo_url":"https://github.com/dadmatech/dadmatools","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"chunking","task_name":"Chunking"},{"task_slug":"constituency-parsing","task_name":"Constituency Parsing"},{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"lemmatization","task_name":"Lemmatization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}