{"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/probabilistic-bag-of-hyperlinks-model-for","title":"Probabilistic Bag-Of-Hyperlinks Model for Entity Linking","arxiv_id":"1509.02301","date":"2015-09-08","proceeding":null,"authors":["Octavian-Eugen Ganea","Marina Ganea","Aurelien Lucchi","Carsten Eickhoff","Thomas Hofmann"],"abstract":"Many fundamental problems in natural language processing rely on determining\nwhat entities appear in a given text. Commonly referenced as entity linking,\nthis step is a fundamental component of many NLP tasks such as text\nunderstanding, automatic summarization, semantic search or machine translation.\nName ambiguity, word polysemy, context dependencies and a heavy-tailed\ndistribution of entities contribute to the complexity of this problem.\n  We here propose a probabilistic approach that makes use of an effective\ngraphical model to perform collective entity disambiguation. Input mentions\n(i.e.,~linkable token spans) are disambiguated jointly across an entire\ndocument by combining a document-level prior of entity co-occurrences with\nlocal information captured from mentions and their surrounding context. The\nmodel is based on simple sufficient statistics extracted from data, thus\nrelying on few parameters to be learned.\n  Our method does not require extensive feature engineering, nor an expensive\ntraining procedure. We use loopy belief propagation to perform approximate\ninference. The low complexity of our model makes this step sufficiently fast\nfor real-time usage. We demonstrate the accuracy of our approach on a wide\nrange of benchmark datasets, showing that it matches, and in many cases\noutperforms, existing state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1509.02301v3","url_pdf":"http://arxiv.org/pdf/1509.02301v3.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":"probabilistic-bag-of-hyperlinks-model-for","repo_url":"https://github.com/dalab/pboh-entity-linking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-disambiguation","task_name":"Entity Disambiguation"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}