{"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/mapping-distributional-to-model-theoretic","title":"Mapping distributional to model-theoretic semantic spaces: a baseline","arxiv_id":"1607.02802","date":"2016-07-11","proceeding":null,"authors":["Franck Dernoncourt"],"abstract":"Word embeddings have been shown to be useful across state-of-the-art systems\nin many natural language processing tasks, ranging from question answering\nsystems to dependency parsing. (Herbelot and Vecchi, 2015) explored word\nembeddings and their utility for modeling language semantics. In particular,\nthey presented an approach to automatically map a standard distributional\nsemantic space onto a set-theoretic model using partial least squares\nregression. We show in this paper that a simple baseline achieves a +51%\nrelative improvement compared to their model on one of the two datasets they\nused, and yields competitive results on the second dataset.","url_abs":"http://arxiv.org/abs/1607.02802v1","url_pdf":"http://arxiv.org/pdf/1607.02802v1.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":"mapping-distributional-to-model-theoretic","repo_url":"https://github.com/Franck-Dernoncourt/model-theoretic","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}