{"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/enhancing-the-lexvec-distributed-word","title":"Enhancing the LexVec Distributed Word Representation Model Using Positional Contexts and External Memory","arxiv_id":"1606.01283","date":"2016-06-03","proceeding":null,"authors":["Alexandre Salle","Marco Idiart","Aline Villavicencio"],"abstract":"In this paper we take a state-of-the-art model for distributed word\nrepresentation that explicitly factorizes the positive pointwise mutual\ninformation (PPMI) matrix using window sampling and negative sampling and\naddress two of its shortcomings. We improve syntactic performance by using\npositional contexts, and solve the need to store the PPMI matrix in memory by\nworking on aggregate data in external memory. The effectiveness of both\nmodifications is shown using word similarity and analogy tasks.","url_abs":"http://arxiv.org/abs/1606.01283v1","url_pdf":"http://arxiv.org/pdf/1606.01283v1.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":"enhancing-the-lexvec-distributed-word","repo_url":"https://github.com/alexandres/lexvec","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-similarity","task_name":"Word Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.01283","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}