{"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/are-distributional-representations-ready-for","title":"Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning","arxiv_id":"1705.11168","date":"2017-05-31","proceeding":"WS 2017 8","authors":["Li Lucy","Jon Gauthier"],"abstract":"Distributional word representation methods exploit word co-occurrences to\nbuild compact vector encodings of words. While these representations enjoy\nwidespread use in modern natural language processing, it is unclear whether\nthey accurately encode all necessary facets of conceptual meaning. In this\npaper, we evaluate how well these representations can predict perceptual and\nconceptual features of concrete concepts, drawing on two semantic norm datasets\nsourced from human participants. We find that several standard word\nrepresentations fail to encode many salient perceptual features of concepts,\nand show that these deficits correlate with word-word similarity prediction\nerrors. Our analyses provide motivation for grounded and embodied language\nlearning approaches, which may help to remedy these deficits.","url_abs":"http://arxiv.org/abs/1705.11168v1","url_pdf":"http://arxiv.org/pdf/1705.11168v1.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":"are-distributional-representations-ready-for","repo_url":"https://github.com/lucy3/Graphs-Embeddings","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"word-similarity","task_name":"Word Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.11168","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}