{"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/semantic-entity-retrieval-toolkit","title":"Semantic Entity Retrieval Toolkit","arxiv_id":"1706.03757","date":"2017-06-12","proceeding":null,"authors":["Christophe Van Gysel","Maarten de Rijke","Evangelos Kanoulas"],"abstract":"Unsupervised learning of low-dimensional, semantic representations of words\nand entities has recently gained attention. In this paper we describe the\nSemantic Entity Retrieval Toolkit (SERT) that provides implementations of our\npreviously published entity representation models. The toolkit provides a\nunified interface to different representation learning algorithms, fine-grained\nparsing configuration and can be used transparently with GPUs. In addition,\nusers can easily modify existing models or implement their own models in the\nframework. After model training, SERT can be used to rank entities according to\na textual query and extract the learned entity/word representation for use in\ndownstream algorithms, such as clustering or recommendation.","url_abs":"http://arxiv.org/abs/1706.03757v2","url_pdf":"http://arxiv.org/pdf/1706.03757v2.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":"semantic-entity-retrieval-toolkit","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":"clustering","task_name":"Clustering"},{"task_slug":"entity-retrieval","task_name":"Entity Retrieval"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"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}