{"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/unsupervised-learning-for-lexicon-based","title":"Unsupervised Learning for Lexicon-Based Classification","arxiv_id":"1611.06933","date":"2016-11-21","proceeding":null,"authors":["Jacob Eisenstein"],"abstract":"In lexicon-based classification, documents are assigned labels by comparing\nthe number of words that appear from two opposed lexicons, such as positive and\nnegative sentiment. Creating such words lists is often easier than labeling\ninstances, and they can be debugged by non-experts if classification\nperformance is unsatisfactory. However, there is little analysis or\njustification of this classification heuristic. This paper describes a set of\nassumptions that can be used to derive a probabilistic justification for\nlexicon-based classification, as well as an analysis of its expected accuracy.\nOne key assumption behind lexicon-based classification is that all words in\neach lexicon are equally predictive. This is rarely true in practice, which is\nwhy lexicon-based approaches are usually outperformed by supervised classifiers\nthat learn distinct weights on each word from labeled instances. This paper\nshows that it is possible to learn such weights without labeled data, by\nleveraging co-occurrence statistics across the lexicons. This offers the best\nof both worlds: light supervision in the form of lexicons, and data-driven\nclassification with higher accuracy than traditional word-counting heuristics.","url_abs":"http://arxiv.org/abs/1611.06933v1","url_pdf":"http://arxiv.org/pdf/1611.06933v1.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":"unsupervised-learning-for-lexicon-based","repo_url":"https://github.com/jacobeisenstein/probabilistic-lexicon-classification","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}