{"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/convolutional-neural-network-with-word","title":"Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation","arxiv_id":"1711.04411","date":"2017-11-13","proceeding":"IJCNLP 2017 11","authors":["Chunqi Wang","Bo Xu"],"abstract":"Character-based sequence labeling framework is flexible and efficient for\nChinese word segmentation (CWS). Recently, many character-based neural models\nhave been applied to CWS. While they obtain good performance, they have two\nobvious weaknesses. The first is that they heavily rely on manually designed\nbigram feature, i.e. they are not good at capturing n-gram features\nautomatically. The second is that they make no use of full word information.\nFor the first weakness, we propose a convolutional neural model, which is able\nto capture rich n-gram features without any feature engineering. For the second\none, we propose an effective approach to integrate the proposed model with word\nembeddings. We evaluate the model on two benchmark datasets: PKU and MSR.\nWithout any feature engineering, the model obtains competitive performance --\n95.7% on PKU and 97.3% on MSR. Armed with word embeddings, the model achieves\nstate-of-the-art performance on both datasets -- 96.5% on PKU and 98.0% on MSR,\nwithout using any external labeled resource.","url_abs":"http://arxiv.org/abs/1711.04411v1","url_pdf":"http://arxiv.org/pdf/1711.04411v1.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":"convolutional-neural-network-with-word","repo_url":"https://github.com/chqiwang/convseg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"chinese-word-segmentation","task_name":"Chinese Word Segmentation"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.04411","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}