{"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/reside-improving-distantly-supervised-neural","title":"RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information","arxiv_id":"1812.04361","date":"2018-12-11","proceeding":"EMNLP 2018 10","authors":["Shikhar Vashishth","Rishabh Joshi","Sai Suman Prayaga","Chiranjib Bhattacharyya","Partha Talukdar"],"abstract":"Distantly-supervised Relation Extraction (RE) methods train an extractor by\nautomatically aligning relation instances in a Knowledge Base (KB) with\nunstructured text. In addition to relation instances, KBs often contain other\nrelevant side information, such as aliases of relations (e.g., founded and\nco-founded are aliases for the relation founderOfCompany). RE models usually\nignore such readily available side information. In this paper, we propose\nRESIDE, a distantly-supervised neural relation extraction method which utilizes\nadditional side information from KBs for improved relation extraction. It uses\nentity type and relation alias information for imposing soft constraints while\npredicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode\nsyntactic information from text and improves performance even when limited side\ninformation is available. Through extensive experiments on benchmark datasets,\nwe demonstrate RESIDE's effectiveness. We have made RESIDE's source code\navailable to encourage reproducible research.","url_abs":"http://arxiv.org/abs/1812.04361v2","url_pdf":"http://arxiv.org/pdf/1812.04361v2.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":"reside-improving-distantly-supervised-neural","repo_url":"https://github.com/malllabiisc/RESIDE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"relationship-extraction-distant-supervised","task_name":"Relationship Extraction (Distant Supervised)"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-nyt-corpus","task":"Relation Extraction","dataset":"NYT Corpus","model":"RESIDE","rank_in_archive_order":5,"of":7,"metrics":{"P@10%":"73.6","P@30%":"59.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.04361","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.04361"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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