{"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/funnelling-a-new-ensemble-method-for","title":"Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification","arxiv_id":"1901.11459","date":"2019-01-31","proceeding":null,"authors":["Andrea Esuli","Alejandro Moreo","Fabrizio Sebastiani"],"abstract":"Cross-lingual Text Classification (CLC) consists of automatically\nclassifying, according to a common set C of classes, documents each written in\none of a set of languages L, and doing so more accurately than when naively\nclassifying each document via its corresponding language-specific classifier.\nIn order to obtain an increase in the classification accuracy for a given\nlanguage, the system thus needs to also leverage the training examples written\nin the other languages. We tackle multilabel CLC via funnelling, a new ensemble\nlearning method that we propose here. Funnelling consists of generating a\ntwo-tier classification system where all documents, irrespectively of language,\nare classified by the same (2nd-tier) classifier. For this classifier all\ndocuments are represented in a common, language-independent feature space\nconsisting of the posterior probabilities generated by 1st-tier,\nlanguage-dependent classifiers. This allows the classification of all test\ndocuments, of any language, to benefit from the information present in all\ntraining documents, of any language. We present substantial experiments, run on\npublicly available multilingual text collections, in which funnelling is shown\nto significantly outperform a number of state-of-the-art baselines. All code\nand datasets (in vector form) are made publicly available.","url_abs":"http://arxiv.org/abs/1901.11459v2","url_pdf":"http://arxiv.org/pdf/1901.11459v2.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":"funnelling-a-new-ensemble-method-for","repo_url":"https://github.com/AlexMoreo/funnelling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"funnelling-a-new-ensemble-method-for","repo_url":"https://github.com/MindCode-4/code-3/tree/main/funnel","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}