{"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/end-to-end-text-classification-via-image","title":"End-to-End Text Classification via Image-based Embedding using Character-level Networks","arxiv_id":"1810.03595","date":"2018-10-08","proceeding":null,"authors":["Shunsuke Kitada","Ryunosuke Kotani","Hitoshi Iyatomi"],"abstract":"For analysing and/or understanding languages having no word boundaries based\non morphological analysis such as Japanese, Chinese, and Thai, it is desirable\nto perform appropriate word segmentation before word embeddings. But it is\ninherently difficult in these languages. In recent years, various language\nmodels based on deep learning have made remarkable progress, and some of these\nmethodologies utilizing character-level features have successfully avoided such\na difficult problem. However, when a model is fed character-level features of\nthe above languages, it often causes overfitting due to a large number of\ncharacter types. In this paper, we propose a CE-CLCNN, character-level\nconvolutional neural networks using a character encoder to tackle these\nproblems. The proposed CE-CLCNN is an end-to-end learning model and has an\nimage-based character encoder, i.e. the CE-CLCNN handles each character in the\ntarget document as an image. Through various experiments, we found and\nconfirmed that our CE-CLCNN captured closely embedded features for visually and\nsemantically similar characters and achieves state-of-the-art results on\nseveral open document classification tasks. In this paper we report the\nperformance of our CE-CLCNN with the Wikipedia title estimation task and\nanalyse the internal behaviour.","url_abs":"http://arxiv.org/abs/1810.03595v2","url_pdf":"http://arxiv.org/pdf/1810.03595v2.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":"end-to-end-text-classification-via-image","repo_url":"https://github.com/IyatomiLab/CE-CLCNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"morphological-analysis","task_name":"Morphological Analysis"},{"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":{"syntology_url":"https://syntology.ai/paper/1810.03595","atlas_url":"https://app.syntology.ai/?focus=1810.03595","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}