{"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/can-ner-convolutional-attention-network","title":"CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition","arxiv_id":"1904.02141","date":"2019-04-03","proceeding":"NAACL 2019 6","authors":["Yuying Zhu","Guoxin Wang","Börje F. Karlsson"],"abstract":"Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.","url_abs":"https://arxiv.org/abs/1904.02141v3","url_pdf":"https://arxiv.org/pdf/1904.02141v3.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":"can-ner-convolutional-attention-network","repo_url":"https://github.com/microsoft/vert-papers/tree/master/papers/CAN-NER","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"chinese-named-entity-recognition","task_name":"Chinese Named Entity Recognition"},{"task_slug":"cg","task_name":"NER"},{"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":"segmentation","task_name":"Segmentation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/chinese-named-entity-recognition-on-msra","task":"Chinese Named Entity Recognition","dataset":"MSRA","model":"CAN-NER Model","rank_in_archive_order":20,"of":21,"metrics":{"F1":"92.97","Precision":"93.53","Recall":"92.42"},"uses_additional_data":false},{"leaderboard":"/sota/chinese-named-entity-recognition-on-ontonotes","task":"Chinese Named Entity Recognition","dataset":"OntoNotes 4","model":"CAN-NER Model","rank_in_archive_order":15,"of":15,"metrics":{"F1":"73.64","Precision":"75.05","Recall":"72.29"},"uses_additional_data":false},{"leaderboard":"/sota/chinese-named-entity-recognition-on-resume","task":"Chinese Named Entity Recognition","dataset":"Resume NER","model":"CAN-NER Model","rank_in_archive_order":12,"of":13,"metrics":{"F1":"94.94","Precision":"95.05","Recall":"94.82"},"uses_additional_data":false},{"leaderboard":"/sota/chinese-named-entity-recognition-on-weibo-ner","task":"Chinese Named Entity Recognition","dataset":"Weibo NER","model":"CAN-NER Model","rank_in_archive_order":18,"of":18,"metrics":{"Accuracy-NE":"55.38","Accuracy-NM":"62.98","Overall":"59.31"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.02141","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}