{"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/parsing-natural-scenes-and-natural-language","title":"Parsing Natural Scenes and Natural Language with Recursive Neural Networks","arxiv_id":null,"date":"2011-06-01","proceeding":"Proceedings of the 26th International Conference on Machine Learning (ICML) 2011 2011 6","authors":["Richard Socher，Cliff Chiung-Yu Lin，Andrew Y. Ng，Christopher D. Manning"],"abstract":"Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences.Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole. We introduce a max-margin structure prediction architecture based on recursive neural networks that can successfully recover such structure both in complex scene images as well as sentences. The same algorithm can be used both to provide a competitive syntactic parser for natural language sentences from the Penn Treebank and to out-perform alternative approaches for semantic scene segmentation, annotation and classification. For segmentation and annotation our algorithm obtains a new level of state-of-the-art performance on the Stanford background dataset (78.1%). The features from the im-age parse tree outperform Gist descriptors forscene classification by 4%.","url_abs":"https://nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf","url_pdf":"https://nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.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":"parsing-natural-scenes-and-natural-language","repo_url":"https://github.com/yihui-he/Parsing-Natural-Scenes-and-Natural-Language-with-Recursive-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"scene-classification","task_name":"Scene Classification"},{"task_slug":"scene-segmentation","task_name":"Scene Segmentation"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}