{"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/classifying-relations-by-ranking-with","title":"Classifying Relations by Ranking with Convolutional Neural Networks","arxiv_id":"1504.06580","date":"2015-04-24","proceeding":"IJCNLP 2015 7","authors":["Cicero Nogueira dos Santos","Bing Xiang","Bo-Wen Zhou"],"abstract":"Relation classification is an important semantic processing task for which\nstate-ofthe-art systems still rely on costly handcrafted features. In this work\nwe tackle the relation classification task using a convolutional neural network\nthat performs classification by ranking (CR-CNN). We propose a new pairwise\nranking loss function that makes it easy to reduce the impact of artificial\nclasses. We perform experiments using the the SemEval-2010 Task 8 dataset,\nwhich is designed for the task of classifying the relationship between two\nnominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art\nfor this dataset and achieve a F1 of 84.1 without using any costly handcrafted\nfeatures. Additionally, our experimental results show that: (1) our approach is\nmore effective than CNN followed by a softmax classifier; (2) omitting the\nrepresentation of the artificial class Other improves both precision and\nrecall; and (3) using only word embeddings as input features is enough to\nachieve state-of-the-art results if we consider only the text between the two\ntarget nominals.","url_abs":"http://arxiv.org/abs/1504.06580v2","url_pdf":"http://arxiv.org/pdf/1504.06580v2.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":"classifying-relations-by-ranking-with","repo_url":"https://github.com/runqitian/CR-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"classifying-relations-by-ranking-with","repo_url":"https://github.com/onehaitao/CR-CNN-relation-extraction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-semeval-2010-task-8","task":"Relation Extraction","dataset":"SemEval-2010 Task-8","model":"CR-CNN","rank_in_archive_order":27,"of":31,"metrics":{"F1":"84.1"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1504.06580","atlas_url":"https://app.syntology.ai/?focus=1504.06580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.06580"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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