{"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/generating-fine-grained-open-vocabulary","title":"Generating Fine-Grained Open Vocabulary Entity Type Descriptions","arxiv_id":"1805.10564","date":"2018-05-27","proceeding":"ACL 2018 7","authors":["Rajarshi Bhowmik","Gerard de Melo"],"abstract":"While large-scale knowledge graphs provide vast amounts of structured facts\nabout entities, a short textual description can often be useful to succinctly\ncharacterize an entity and its type. Unfortunately, many knowledge graph\nentities lack such textual descriptions. In this paper, we introduce a dynamic\nmemory-based network that generates a short open vocabulary description of an\nentity by jointly leveraging induced fact embeddings as well as the dynamic\ncontext of the generated sequence of words. We demonstrate the ability of our\narchitecture to discern relevant information for more accurate generation of\ntype description by pitting the system against several strong baselines.","url_abs":"http://arxiv.org/abs/1805.10564v1","url_pdf":"http://arxiv.org/pdf/1805.10564v1.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":"generating-fine-grained-open-vocabulary","repo_url":"https://github.com/kingsaint/Open-vocabulary-entity-type-description","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.10564","atlas_url":"https://app.syntology.ai/?focus=1805.10564","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}