{"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/non-distributional-word-vector","title":"Non-distributional Word Vector Representations","arxiv_id":"1506.05230","date":"2015-06-17","proceeding":"IJCNLP 2015 7","authors":["Manaal Faruqui","Chris Dyer"],"abstract":"Data-driven representation learning for words is a technique of central\nimportance in NLP. While indisputably useful as a source of features in\ndownstream tasks, such vectors tend to consist of uninterpretable components\nwhose relationship to the categories of traditional lexical semantic theories\nis tenuous at best. We present a method for constructing interpretable word\nvectors from hand-crafted linguistic resources like WordNet, FrameNet etc.\nThese vectors are binary (i.e, contain only 0 and 1) and are 99.9% sparse. We\nanalyze their performance on state-of-the-art evaluation methods for\ndistributional models of word vectors and find they are competitive to standard\ndistributional approaches.","url_abs":"http://arxiv.org/abs/1506.05230v1","url_pdf":"http://arxiv.org/pdf/1506.05230v1.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":"non-distributional-word-vector","repo_url":"https://github.com/mfaruqui/non-distributional","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.05230","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}