{"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/semantic-word-clusters-using-signed","title":"Semantic Word Clusters Using Signed Normalized Graph Cuts","arxiv_id":"1601.05403","date":"2016-01-20","proceeding":null,"authors":["João Sedoc","Jean Gallier","Lyle Ungar","Dean Foster"],"abstract":"Vector space representations of words capture many aspects of word\nsimilarity, but such methods tend to make vector spaces in which antonyms (as\nwell as synonyms) are close to each other. We present a new signed spectral\nnormalized graph cut algorithm, signed clustering, that overlays existing\nthesauri upon distributionally derived vector representations of words, so that\nantonym relationships between word pairs are represented by negative weights.\nOur signed clustering algorithm produces clusters of words which simultaneously\ncapture distributional and synonym relations. We evaluate these clusters\nagainst the SimLex-999 dataset (Hill et al.,2014) of human judgments of word\npair similarities, and also show the benefit of using our clusters to predict\nthe sentiment of a given text.","url_abs":"http://arxiv.org/abs/1601.05403v1","url_pdf":"http://arxiv.org/pdf/1601.05403v1.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":"semantic-word-clusters-using-signed","repo_url":"https://github.com/jsedoc/SignedSpectralClustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"word-similarity","task_name":"Word Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}