|
|||||
|
|
| 基于HowNet的语义表示学习 | |
| 引用本文： | 朱靖雯,杨玉基,许斌,李涓子. 基于HowNet的语义表示学习[J]. 中文信息学报, 2019, 33(3): 33-41 |
| 作者姓名： | 朱靖雯 杨玉基 许斌 李涓子 |
| 作者单位： | 1.北京信息科技大学 信息管理学院,北京 100192; 2.清华大学 计算机系知识工程实验室,北京 100084 |
| 基金项目： | 国家高技术研究发展计划(863)(2015AA015401);国家科技部重点研发计划(2018YFB100283) |
| 摘 要： | HowNet是一个大规模高质量的跨语言(中英)常识知识库,蕴含着丰富的语义信息。该文利用知识图谱领域的方法将HowNet复杂的结构层层拆解,得到了知识图谱形式的HownetGraph,进而利用网络表示学习以及知识表示学习方法得到了跨语言(中、英)、跨语义单位(字词、义项①、DEF_CONCEPT②和义原)的向量表示,在词语相似度(word similarity)和词语类比(word analogy)任务上对中英文数据集进行了实验,实验结果显示该文提出的方法在词语语义相似度的任务上取得了最好效果。 |
| 关 键 词： | HOWNET 知识图谱 语义表示 表示学习 |
Semantic Representation Learning Based on HowNet |
|
| ZHU Jingwen,YANG Yuji,XU Bin,LI Juanzi. Semantic Representation Learning Based on HowNet[J]. Journal of Chinese Information Processing, 2019, 33(3): 33-41 | |
| Authors: | ZHU Jingwen YANG Yuji XU Bin LI Juanzi |
| Affiliation: | 1.School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China; 2.Knowledge Engineering Group, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China |
| Abstract: | HowNet is a large-scale and high-quality cross-lingual commonsense knowledge base, containing a wealth of semantic information. This paper disassembles HowNets complex structure and obtains HownetGraph in the form of knowledge graph. Then Network Representation Learning and Knowledge Representation Learning methods are applied to obtain cross-lingual vector representation of different semantic units, i.e., word, sense, DEF_CONCEPT and sememe. Two series of experiments (word similarity and word analogy) are conducted on Chinese and English datasets, and the results show the proposed method achieves the best results. |
| Keywords: | HowNet knowledge graph semantic representation representation learning |
| 本文献已被 维普 等数据库收录！ | |
| 点击此处可从《中文信息学报》浏览原始摘要信息 | |
| 点击此处可从《中文信息学报》下载全文 | |