{"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/higher-order-relation-schema-induction-using","title":"Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation","arxiv_id":"1707.01917","date":"2017-07-06","proceeding":"ACL 2018 7","authors":["Madhav Nimishakavi","Partha Talukdar"],"abstract":"Relation Schema Induction (RSI) is the problem of identifying type signatures\nof arguments of relations from unlabeled text. Most of the previous work in\nthis area have focused only on binary RSI, i.e., inducing only the subject and\nobject type signatures per relation. However, in practice, many relations are\nhigh-order, i.e., they have more than two arguments and inducing type\nsignatures of all arguments is necessary. For example, in the sports domain,\ninducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is\nmore informative than inducing just win(WinningPlayer, OpponentPlayer). We\nrefer to this problem as Higher-order Relation Schema Induction (HRSI). In this\npaper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a\nnovel framework for the HRSI problem. To the best of our knowledge, this is the\nfirst attempt at inducing higher-order relation schemata from unlabeled text.\nUsing the experimental analysis on three real world datasets, we show how TFBA\nhelps in dealing with sparsity and induce higher order schemata.","url_abs":"http://arxiv.org/abs/1707.01917v2","url_pdf":"http://arxiv.org/pdf/1707.01917v2.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":"higher-order-relation-schema-induction-using","repo_url":"https://github.com/madhavcsa/TFBA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}