Exploiting global contextual information for document-level named entity recognition
文献类型：期刊论文
| 作者 | Yu, Yiting5; Wang, Zanbo5; Wei, Wei5; Zhang, Ruihan5; Mao, Xian-Ling4; Feng, Shanshan3; Wang, Fei2; He, Zhiyong1; Jiang, Sheng5 |
| 刊名 | KNOWLEDGE-BASED SYSTEMS
 |
| 出版日期 | 2024-01-25 |
| 卷号 | 284页码:10 |
| 关键词 | Named entity recognition Global contextual information Graph neural network Epistemic uncertainty |
| ISSN号 | 0950-7051 |
| DOI | 10.1016/j.knosys.2023.111266 |
| 英文摘要 | Named entity recognition (NER, also known as entity chunking/extraction) is a fundamental sub-task of information extraction, which aims at identifying named entities from an unstructured text into pre-defined classes. Most of the existing works mainly focus on modeling local-context dependencies in a single sentence for entity type prediction. However, they may neglect the clues derived from other sentences within a document, and thus suffer from the sentence-level inherent ambiguity issue, which may make their performance drop to some extent. To this end, we propose a Global Context enhanced Document-level NER (GCDoc) model for NER to fully exploit the global contextual information of a document in different levels, i.e., word-level and sentence-level. Specifically, GCDoc constructs a document graph to capture the global dependencies of words for enriching the representations of each word in word-level. Then, it encodes the adjacent sentences for exploring the contexts across sentences to enhance the representation of the current sentence via the specially devised attention mechanism. Extensive experiments on two benchmark NER datasets (i.e., CoNLL 2003 and Onenotes 5.0 English dataset) demonstrate the effectiveness of our proposed model, as compared to the competitive baselines. |
| 资助项目 | National Natural Science Foundation of China[62276110] ; National Natural Science Foundation of China[62172039] ; Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL) |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001132970800001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38433]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wei, Wei |
| 作者单位 | 1.Naval Univ Engn, Sch Elect Engn, Wuhan, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.ASTAR, Ctr Frontier AI Res, IHPC, Singapore City, Singapore 4.Beijing Inst Technol, Dept Comp Sci & Technol, Beijing, Peoples R China 5.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cognit Comp & Intelligent Informat Proc CCIIP Lab, Wuhan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Yiting,Wang, Zanbo,Wei, Wei,et al. Exploiting global contextual information for document-level named entity recognition[J]. KNOWLEDGE-BASED SYSTEMS,2024,284:10. |
| APA | Yu, Yiting.,Wang, Zanbo.,Wei, Wei.,Zhang, Ruihan.,Mao, Xian-Ling.,...&Jiang, Sheng.(2024).Exploiting global contextual information for document-level named entity recognition.KNOWLEDGE-BASED SYSTEMS,284,10. |
| MLA | Yu, Yiting,et al."Exploiting global contextual information for document-level named entity recognition".KNOWLEDGE-BASED SYSTEMS 284(2024):10. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。