浅析自然语言处理技术在文本挖掘中的应用及其挑战
摘 要：文章对如何将自然语言处理技术用于文本挖掘进行了探讨，并提出了一些新的解决方案。自然语言处理作为一种新兴的方法，能够提取关键信息，进行情感分析，并对话题进行分类，在新闻报道、社会媒体分析等方面具有重要的应用前景。该方法不但能提高信息处理的效率，而且能帮助人们更好地了解民意，辅助政策制定。但是，在实际应用中，数据的稀疏性、语义的模糊性以及跨语言的处理等问题仍然是一个亟待解决的问题。针对上述问题，本项目拟从集成的数据增强和预训练模型、融合上下文和多模态信息的融合、跨语言处理的框架等方面展开研究。通过本项目的研究，将进一步提升模型的泛化性能，突破现有方法中存在的问题，促进跨语言文本挖掘技术的发展。本项目的研究成果将为更好地将自然语言处理技术引入到文本挖掘中，从而促进相关领域的技术进步。
关键词：自然语言技术；文本挖掘；集成数据
The application of natural language processing technology in text mining
Abstract ：This paper discusses how to use natural language processing technology for text mining, and proposes some new solutions. As an emerging method, natural language processing can extract key information, conduct emotion analysis, and classify topics, which has important application prospects in news reporting, social media analysis and other aspects. This method can not only improve the efficiency of information processing, but also help people better understand public opinion and assist policy making. However, in practical applications, data sparsity, semantic ambiguity and cross-language processing are still urgent problems to be solved. In view of the above problems, this project intends to study from the aspects of integrated data enhancement and pre-training model, the fusion of fusion context and multimodal information, and the fr amework of cross-language processing. Through the research of this project, it will further improve the generalization performance of the model, break through the existing problems, and promote the development of cross-language text mining technology. The research results of this project will be designed to better introduce natural language processing technology into text mining, and thus promote technological advances in related fields.
Key Words ： Natural language technology; Text mining; Integrated data
目 录
摘要 1
1 NLP的基本原理 2
1.1 关于 NLP的基础知识 2
1.2 Language Model和 Text表达 3
2 用于文本挖掘的自然语言处理技术 3
2.1 运用于新闻报道 3
2.2 将其用于社会媒体的研究 4
3 面向自然语言处理的文本挖掘研究 4
3.1 数据稀疏性 4
3.2 语义不明确 5
3.3 多语种过程 5
4 为应对已存在的难题而采取的最优战略 6
4.1 集成化的数据增强和预培训模式 6
4.2 融合语境和多模态 6
4.3 建立一个跨平台的处理架构 7
5 结论 7
参考文献 8
谢辞 10
