{"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/encoding-category-trees-into-word-embeddings","title":"Encoding Category Trees Into Word-Embeddings Using Geometric Approach","arxiv_id":null,"date":"2019-05-01","proceeding":"ICLR 2019 5","authors":["Tiansi Dong","Olaf Cremers","Hailong Jin","Juanzi Li","Chrisitan Bauckhage","Armin B. Cremers","Daniel Speicher","Joerg Zimmermann"],"abstract":"We present a novel method to implicitly encode a tree-structured category information into word-embeddings, resulting in super-dimensional ball representations ($n$-ball embedding for short). Inclusion relations among $n$-balls precisely encode subordinate relations among categories. The cosine  similarity function is enriched by category information. A large $n$-ball dataset is constructed using geometrical method, which achieves zero energy cost in embedding tree structures into word embedding. A new benchmark dataset is created for predicting the category of unknown words. Experiments show that $n$-ball embeddings, carried with category information, significantly out-perform word-embeddings in the neighbourhood test, while only slightly change the original word-embeddings. Experiment results also show that $n$-ball embeddings demonstrate surprisingly good performance in validating the category of unknown word. Source codes and data-sets are free for public access \\url{https://github.com/gnodisnait/nball4tree.git} and \\url{https://github.com/gnodisnait/bp94nball.git}. ","url_abs":"https://openreview.net/forum?id=rJlWOj0qF7","url_pdf":"https://openreview.net/pdf?id=rJlWOj0qF7","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":"encoding-category-trees-into-word-embeddings","repo_url":"https://github.com/gnodisnait/bp94nball","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"encoding-category-trees-into-word-embeddings","repo_url":"https://github.com/gnodisnait/nball4tree","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}