{"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/semi-supervised-convolutional-neural-networks-1","title":"Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding","arxiv_id":"1504.01255","date":"2015-04-06","proceeding":"NeurIPS 2015 12","authors":["Rie Johnson","Tong Zhang"],"abstract":"This paper presents a new semi-supervised framework with convolutional neural\nnetworks (CNNs) for text categorization. Unlike the previous approaches that\nrely on word embeddings, our method learns embeddings of small text regions\nfrom unlabeled data for integration into a supervised CNN. The proposed scheme\nfor embedding learning is based on the idea of two-view semi-supervised\nlearning, which is intended to be useful for the task of interest even though\nthe training is done on unlabeled data. Our models achieve better results than\nprevious approaches on sentiment classification and topic classification tasks.","url_abs":"http://arxiv.org/abs/1504.01255v3","url_pdf":"http://arxiv.org/pdf/1504.01255v3.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"text-categorization","task_name":"Text Categorization"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"topic-classification","task_name":"Topic Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-classification-on-imdb","task":"Text Classification","dataset":"IMDb","model":"Transductive SVM Johnson & Zhang ([2015b])","rank_in_archive_order":6,"of":13,"metrics":{},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}