{"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/comparative-study-of-cnn-and-rnn-for-natural","title":"Comparative Study of CNN and RNN for Natural Language Processing","arxiv_id":"1702.01923","date":"2017-02-07","proceeding":null,"authors":["Wenpeng Yin","Katharina Kann","Mo Yu","Hinrich Schütze"],"abstract":"Deep neural networks (DNN) have revolutionized the field of natural language\nprocessing (NLP). Convolutional neural network (CNN) and recurrent neural\nnetwork (RNN), the two main types of DNN architectures, are widely explored to\nhandle various NLP tasks. CNN is supposed to be good at extracting\nposition-invariant features and RNN at modeling units in sequence. The state of\nthe art on many NLP tasks often switches due to the battle between CNNs and\nRNNs. This work is the first systematic comparison of CNN and RNN on a wide\nrange of representative NLP tasks, aiming to give basic guidance for DNN\nselection.","url_abs":"http://arxiv.org/abs/1702.01923v1","url_pdf":"http://arxiv.org/pdf/1702.01923v1.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":"comparative-study-of-cnn-and-rnn-for-natural","repo_url":"https://github.com/Msundarv/TwiLoc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"comparative-study-of-cnn-and-rnn-for-natural","repo_url":"https://github.com/RonRaifer/BERT-Ghazali","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"comparative-study-of-cnn-and-rnn-for-natural","repo_url":"https://github.com/hbahadirsahin/text_categorization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"comparative-study-of-cnn-and-rnn-for-natural","repo_url":"https://github.com/joshuaczhao/CNN-Sentence-Classifier-Reproduction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1702.01923","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}