{"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/vcwe-visual-character-enhanced-word","title":"VCWE: Visual Character-Enhanced Word Embeddings","arxiv_id":"1902.08795","date":"2019-02-23","proceeding":"NAACL 2019 6","authors":["Chi Sun","Xipeng Qiu","Xuanjing Huang"],"abstract":"Chinese is a logographic writing system, and the shape of Chinese characters\ncontain rich syntactic and semantic information. In this paper, we propose a\nmodel to learn Chinese word embeddings via three-level composition: (1) a\nconvolutional neural network to extract the intra-character compositionality\nfrom the visual shape of a character; (2) a recurrent neural network with\nself-attention to compose character representation into word embeddings; (3)\nthe Skip-Gram framework to capture non-compositionality directly from the\ncontextual information. Evaluations demonstrate the superior performance of our\nmodel on four tasks: word similarity, sentiment analysis, named entity\nrecognition and part-of-speech tagging.","url_abs":"http://arxiv.org/abs/1902.08795v2","url_pdf":"http://arxiv.org/pdf/1902.08795v2.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":"vcwe-visual-character-enhanced-word","repo_url":"https://github.com/HSLCY/VCWE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"word-similarity","task_name":"Word Similarity"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.08795","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}