{"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/local-word-vectors-guiding-keyphrase","title":"Local Word Vectors Guiding Keyphrase Extraction","arxiv_id":"1710.07503","date":"2017-10-20","proceeding":null,"authors":["Eirini Papagiannopoulou","Grigorios Tsoumakas"],"abstract":"Automated keyphrase extraction is a fundamental textual information\nprocessing task concerned with the selection of representative phrases from a\ndocument that summarize its content. This work presents a novel unsupervised\nmethod for keyphrase extraction, whose main innovation is the use of local word\nembeddings (in particular GloVe vectors), i.e., embeddings trained from the\nsingle document under consideration. We argue that such local representation of\nwords and keyphrases are able to accurately capture their semantics in the\ncontext of the document they are part of, and therefore can help in improving\nkeyphrase extraction quality. Empirical results offer evidence that indeed\nlocal representations lead to better keyphrase extraction results compared to\nboth embeddings trained on very large third corpora or larger corpora\nconsisting of several documents of the same scientific field and to other\nstate-of-the-art unsupervised keyphrase extraction methods.","url_abs":"http://arxiv.org/abs/1710.07503v4","url_pdf":"http://arxiv.org/pdf/1710.07503v4.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":"local-word-vectors-guiding-keyphrase","repo_url":"https://github.com/epapagia/RVA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"keyphrase-extraction","task_name":"Keyphrase Extraction"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"glove","method_name":"GloVe"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}