{"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/compositional-coding-capsule-network-with-k","title":"Compositional Coding Capsule Network with K-Means Routing for Text Classification","arxiv_id":"1810.09177","date":"2018-10-22","proceeding":null,"authors":["Hao Ren","Hong Lu"],"abstract":"Text classification is a challenging problem which aims to identify the category of texts. In the process of training, word embeddings occupy a large part of parameters. Under the limitation of limited computing resources, it indirectly limits the ability of subsequent network designs. In order to reduce the number of parameters, the compositional coding mechanism has been proposed recently. Based on this, this paper further explores compositional coding and proposes a compositional weighted coding method. And we apply capsule network to model the relationship between word embeddings, a new routing algorithm, which is based on k-means clustering theory, is proposed to fully mine the relationship between word embeddings. Combined with our compositional weighted coding method and the routing algorithm, we design a neural network for text classification. Experiments conducted on eight challenging text classification datasets show that the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.","url_abs":"https://arxiv.org/abs/1810.09177v5","url_pdf":"https://arxiv.org/pdf/1810.09177v5.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":"compositional-coding-capsule-network-with-k","repo_url":"https://github.com/leftthomas/CCCapsNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[{"method_slug":"fixcaps","method_name":"Capsule Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-amazon-review-full","task":"Sentiment Analysis","dataset":"Amazon Review Full","model":"CCCapsNet","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy":"60.95"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-amazon-review-polarity","task":"Sentiment Analysis","dataset":"Amazon Review Polarity","model":"CCCapsNet","rank_in_archive_order":7,"of":9,"metrics":{"Accuracy":"94.96"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-yelp-binary","task":"Sentiment Analysis","dataset":"Yelp Binary classification","model":"CCCapsNet","rank_in_archive_order":12,"of":20,"metrics":{"Error":"3.52"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-yelp-fine-grained","task":"Sentiment Analysis","dataset":"Yelp Fine-grained classification","model":"CCCapsNet","rank_in_archive_order":11,"of":17,"metrics":{"Error":"34.15"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-ag-news","task":"Text Classification","dataset":"AG News","model":"CCCapsNet","rank_in_archive_order":15,"of":24,"metrics":{"Error":"7.61"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-dbpedia","task":"Text Classification","dataset":"DBpedia","model":"CCCapsNet","rank_in_archive_order":16,"of":21,"metrics":{"Error":"1.28"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-sogou-news","task":"Text Classification","dataset":"Sogou News","model":"CCCapsNet","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"97.25"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-yahoo-answers","task":"Text Classification","dataset":"Yahoo! Answers","model":"CCCapsNet","rank_in_archive_order":7,"of":10,"metrics":{"Accuracy":"73.85"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}