| 摘要: |
| 自然语言处理是人工智能的核心技术, 文本表示是自然语言处理的基础性和必要性工作, 影响甚至决定着自然语言处理系统的质量和性能. 探讨了文本表示的基本原理、自然语言的形式化、语言模型以及文本表示的内涵和外延. 宏观上分析了文本表示的技术分类, 对主流技术和方法, 包括基于向量空间、基于主题模型、基于图、基于神经网络、基于表示学习的文本表示, 进行了分析、归纳和总结, 对基于事件、基于语义和基于知识的文本表示也进行了介绍. 对文本表示技术的发展趋势和方向进行了预测和进一步讨论. 以神经网络为基础的深度学习以及表示学习在文本表示中将发挥重要作用, 预训练加调优的策略将逐渐成为主流, 文本表示需要具体问题具体分析, 技术和应用融合是推动力. |
| 关键词: 自然语言处理 文本表示 向量空间模型 主题模型 图模型 深度学习 表示学习 |
| DOI：10.13328/j.cnki.jos.006304 |
| 分类号:TP391 |
| 基金项目:国家自然科学基金(61773276, 61836007) |
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| Research on Text Representation in Natural Language Processing |
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ZHAO Jing-Sheng1,2, SONG Meng-Xue1, GAO Xiang1, ZHU Qiao-Ming2
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1.School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China;2.School of Computer Science and Technology, Soochow University, Suzhou 215021, China
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| Abstract: |
| Natural language processing is the core technology of artificial intelligence. Text representation is the basic and necessary work of natural language processing, which affects or even determines the quality and performance of natural language processing systems. This study discusses the basic principle of text representation, the formalization of natural language, the language model, and the connotation and extension of text representation. The technical classification of text representation on a macro level is analyzed. The mainstreams of text representation technologies and methods are analyzed, induced and summarized, including vector space model, topic model, graph-based model, neural network-based model, and representation learning. Event-based, semantic-based, and knowledge-based text representation technologies are also introduced. The development trends and directions of text representation technology are predicted and further discussed. Neural network-based deep learning and representation learning on text will play an important role in natural language processing. The strategy of pre-training and fine-tune optimization will gradually become the mainstream technology. Text representation needs specific analysis according to specific problems. The integration of technology and application is the driving force. |
| Key words: natural language processing text representation vector space model topic model graph model deep learning representation learning |
