{"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-efficient-and-semantic-expertise","title":"Unsupervised, Efficient and Semantic Expertise Retrieval","arxiv_id":"1608.06651","date":"2016-08-23","proceeding":null,"authors":["Christophe Van Gysel","Maarten de Rijke","Marcel Worring"],"abstract":"We introduce an unsupervised discriminative model for the task of retrieving\nexperts in online document collections. We exclusively employ textual evidence\nand avoid explicit feature engineering by learning distributed word\nrepresentations in an unsupervised way. We compare our model to\nstate-of-the-art unsupervised statistical vector space and probabilistic\ngenerative approaches. Our proposed log-linear model achieves the retrieval\nperformance levels of state-of-the-art document-centric methods with the low\ninference cost of so-called profile-centric approaches. It yields a\nstatistically significant improved ranking over vector space and generative\nmodels in most cases, matching the performance of supervised methods on various\nbenchmarks. That is, by using solely text we can do as well as methods that\nwork with external evidence and/or relevance feedback. A contrastive analysis\nof rankings produced by discriminative and generative approaches shows that\nthey have complementary strengths due to the ability of the unsupervised\ndiscriminative model to perform semantic matching.","url_abs":"http://arxiv.org/abs/1608.06651v2","url_pdf":"http://arxiv.org/pdf/1608.06651v2.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-efficient-and-semantic-expertise","repo_url":"https://github.com/cvangysel/SERT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}