{"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/yake-keyword-extraction-from-single-documents","title":"YAKE! Keyword extraction from single documents using multiple local features","arxiv_id":null,"date":"2018-03-01","proceeding":"ECIR 2018 2018 3","authors":["Ricardo Campos","Vítor Mangaravite","Arian Pasquali","A. Jorge","C. Nunes","A. Jatowt"],"abstract":"In this paper, we present YAKE!, a novel feature-based system for\r\nmulti-lingual keyword extraction from single documents, which supports texts\r\nof different sizes, domains or languages. Unlike most systems, YAKE! does not\r\nrely on dictionaries or thesauri, neither it is trained against any corpora. Instead,\r\nwe follow an unsupervised approach which builds upon features extracted from\r\nthe text, making it thus applicable to documents written in many different languages without the need for external knowledge. This can be beneficial for a large number of tasks and a plethora of situations where the access to training\r\ncorpora is either limited or restricted. In this demo, we offer an easy to use,\r\ninteractive session, where users from both academia and industry can try our\r\nsystem, either by using a sample document or by introducing their own text. As\r\nan add-on, we compare our extracted keywords against the output produced by\r\nthe IBM Natural Language Understanding (IBM NLU) and Rake system.\r\nYAKE! demo is available at http://bit.ly/YakeDemoECIR2018. A python\r\nimplementation of YAKE! is also available at PyPi repository (https://pypi.\r\npython.org/pypi/yake/).","url_abs":"https://repositorio.inesctec.pt/server/api/core/bitstreams/ef121a01-a0a6-4be8-945d-3324a58fc944/content","url_pdf":"https://repositorio.inesctec.pt/server/api/core/bitstreams/ef121a01-a0a6-4be8-945d-3324a58fc944/content","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":"yake-keyword-extraction-from-single-documents","repo_url":"https://github.com/LIAAD/yake","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"keyword-extraction","task_name":"Keyword Extraction"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}