{"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/relation-classification-via-recurrent-neural","title":"Relation Classification via Recurrent Neural Network","arxiv_id":"1508.01006","date":"2015-08-05","proceeding":null,"authors":["Dongxu Zhang","Dong Wang"],"abstract":"Deep learning has gained much success in sentence-level relation\nclassification. For example, convolutional neural networks (CNN) have delivered\ncompetitive performance without much effort on feature engineering as the\nconventional pattern-based methods. Thus a lot of works have been produced\nbased on CNN structures. However, a key issue that has not been well addressed\nby the CNN-based method is the lack of capability to learn temporal features,\nespecially long-distance dependency between nominal pairs. In this paper, we\npropose a simple framework based on recurrent neural networks (RNN) and compare\nit with CNN-based model. To show the limitation of popular used SemEval-2010\nTask 8 dataset, we introduce another dataset refined from MIMLRE(Angeli et al.,\n2014). Experiments on two different datasets strongly indicates that the\nRNN-based model can deliver better performance on relation classification, and\nit is particularly capable of learning long-distance relation patterns. This\nmakes it suitable for real-world applications where complicated expressions are\noften involved.","url_abs":"http://arxiv.org/abs/1508.01006v2","url_pdf":"http://arxiv.org/pdf/1508.01006v2.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":"relation-classification-via-recurrent-neural","repo_url":"https://github.com/DavidMortensen/Bachelor-readinglist","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1508.01006","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}