{"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/weakly-supervised-hierarchical-text","title":"Weakly-Supervised Hierarchical Text Classification","arxiv_id":"1812.11270","date":"2018-12-29","proceeding":null,"authors":["Yu Meng","Jiaming Shen","Chao Zhang","Jiawei Han"],"abstract":"Hierarchical text classification, which aims to classify text documents into\na given hierarchy, is an important task in many real-world applications.\nRecently, deep neural models are gaining increasing popularity for text\nclassification due to their expressive power and minimum requirement for\nfeature engineering. However, applying deep neural networks for hierarchical\ntext classification remains challenging, because they heavily rely on a large\namount of training data and meanwhile cannot easily determine appropriate\nlevels of documents in the hierarchical setting. In this paper, we propose a\nweakly-supervised neural method for hierarchical text classification. Our\nmethod does not require a large amount of training data but requires only\neasy-to-provide weak supervision signals such as a few class-related documents\nor keywords. Our method effectively leverages such weak supervision signals to\ngenerate pseudo documents for model pre-training, and then performs\nself-training on real unlabeled data to iteratively refine the model. During\nthe training process, our model features a hierarchical neural structure, which\nmimics the given hierarchy and is capable of determining the proper levels for\ndocuments with a blocking mechanism. Experiments on three datasets from\ndifferent domains demonstrate the efficacy of our method compared with a\ncomprehensive set of baselines.","url_abs":"http://arxiv.org/abs/1812.11270v1","url_pdf":"http://arxiv.org/pdf/1812.11270v1.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":"weakly-supervised-hierarchical-text","repo_url":"https://github.com/yumeng5/WeSHClass","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"blocking","task_name":"Blocking"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.11270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.11270"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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