{"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/deep-short-text-classification-with-knowledge","title":"Deep Short Text Classification with Knowledge Powered Attention","arxiv_id":"1902.08050","date":"2019-02-21","proceeding":null,"authors":["Jindong Chen","Yizhou Hu","Jingping Liu","Yanghua Xiao","Haiyun Jiang"],"abstract":"Short text classification is one of important tasks in Natural Language\nProcessing (NLP). Unlike paragraphs or documents, short texts are more\nambiguous since they have not enough contextual information, which poses a\ngreat challenge for classification. In this paper, we retrieve knowledge from\nexternal knowledge source to enhance the semantic representation of short\ntexts. We take conceptual information as a kind of knowledge and incorporate it\ninto deep neural networks. For the purpose of measuring the importance of\nknowledge, we introduce attention mechanisms and propose deep Short Text\nClassification with Knowledge powered Attention (STCKA). We utilize Concept\ntowards Short Text (C- ST) attention and Concept towards Concept Set (C-CS)\nattention to acquire the weight of concepts from two aspects. And we classify a\nshort text with the help of conceptual information. Unlike traditional\napproaches, our model acts like a human being who has intrinsic ability to make\ndecisions based on observation (i.e., training data for machines) and pays more\nattention to important knowledge. We also conduct extensive experiments on four\npublic datasets for different tasks. The experimental results and case studies\nshow that our model outperforms the state-of-the-art methods, justifying the\neffectiveness of knowledge powered attention.","url_abs":"http://arxiv.org/abs/1902.08050v1","url_pdf":"http://arxiv.org/pdf/1902.08050v1.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":"deep-short-text-classification-with-knowledge","repo_url":"https://github.com/AIRobotZhang/STCKA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"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=1902.08050","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}