{"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/jb132-submission-to-the-sigmorphon-2022","title":"JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morphological Segmentation","arxiv_id":null,"date":"2022-07-01","proceeding":"NAACL (SIGMORPHON) 2022 7","authors":["Jan Bodnár"],"abstract":"This paper describes the JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morpheme Segmentation. In this paper we describe probabilistic model trained with the Expectation-Maximization algorithm, we provide the results and analyze sources of errors and general limitations of our approach. The model was implemented within our own modular probabilistic framework.","url_abs":"https://aclanthology.org/2022.sigmorphon-1.17","url_pdf":"https://aclanthology.org/2022.sigmorphon-1.17.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":[],"tasks":[{"task_slug":"morpheme-segmentaiton","task_name":"Morpheme Segmentaiton"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/morpheme-segmentaiton-on-unimorph-4-0","task":"Morpheme Segmentaiton","dataset":"UniMorph 4.0","model":"HMM (JB132; trained w EM)","rank_in_archive_order":7,"of":19,"metrics":{"macro avg (subtask 1)":"58.39"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}