{"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/boundary-based-mwe-segmentation-with-text-1","title":"Boundary-based MWE segmentation with text partitioning","arxiv_id":"1608.02025","date":"2016-08-05","proceeding":null,"authors":["Jake Ryland Williams"],"abstract":"This work presents a fine-grained, text-chunking algorithm designed for the\ntask of multiword expressions (MWEs) segmentation. As a lexical class, MWEs\ninclude a wide variety of idioms, whose automatic identification are a\nnecessity for the handling of colloquial language. This algorithm's core\nnovelty is its use of non-word tokens, i.e., boundaries, in a bottom-up\nstrategy. Leveraging boundaries refines token-level information, forging\nhigh-level performance from relatively basic data. The generality of this\nmodel's feature space allows for its application across languages and domains.\nExperiments spanning 19 different languages exhibit a broadly-applicable,\nstate-of-the-art model. Evaluation against recent shared-task data places text\npartitioning as the overall, best performing MWE segmentation algorithm,\ncovering all MWE classes and multiple English domains (including user-generated\ntext). This performance, coupled with a non-combinatorial, fast-running design,\nproduces an ideal combination for implementations at scale, which are\nfacilitated through the release of open-source software.","url_abs":"http://arxiv.org/abs/1608.02025v3","url_pdf":"http://arxiv.org/pdf/1608.02025v3.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":"boundary-based-mwe-segmentation-with-text-1","repo_url":"https://github.com/jakerylandwilliams/partitioner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"chunking","task_name":"Chunking"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}