{"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/all-in-1-short-text-classification-with-one","title":"ALL-IN-1: Short Text Classification with One Model for All Languages","arxiv_id":"1710.09589","date":"2017-10-26","proceeding":null,"authors":["Barbara Plank"],"abstract":"We present ALL-IN-1, a simple model for multilingual text classification that\ndoes not require any parallel data. It is based on a traditional Support Vector\nMachine classifier exploiting multilingual word embeddings and character\nn-grams. Our model is simple, easily extendable yet very effective, overall\nranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer\nfeedback analysis in four languages: English, French, Japanese and Spanish.","url_abs":"http://arxiv.org/abs/1710.09589v1","url_pdf":"http://arxiv.org/pdf/1710.09589v1.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":"all-in-1-short-text-classification-with-one","repo_url":"https://github.com/bplank/ijcnlp2017-customer-feedback","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multilingual-word-embeddings","task_name":"Multilingual Word Embeddings"},{"task_slug":"multilingual-text-classification","task_name":"Multilingual text classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"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}