摘 要：随着信息技术的迅猛发展，自然语言处理成为人工智能领域的重要研究方向。本研究旨在基于机器学习探索自然语言处理的新方法与应用，以提高文本理解、语义分析等任务的效果。通过对比多种经典机器学习算法，选用适合自然语言处理任务的模型，并引入深度学习中的神经网络结构优化传统算法，构建了高效的自然语言处理框架。在实验部分，采用大规模语料库进行训练和测试，针对文本分类、情感分析、命名实体识别等典型任务展开研究。结果表明，所提出的方法在准确率、召回率等方面较传统方法有显著提升。创新性地将迁移学习应用于小样本场景下的自然语言处理，有效缓解数据稀缺问题，为解决特定领域的自然语言处理任务提供了新思路，主要贡献在于融合多种机器学习技术形成综合解决方案，推动自然语言处理向更智能、更高效的方向发展。
关键词：自然语言处理；机器学习；深度学习；文本分类；迁移学习
Research on Natural Language Processing Based on Machine Learning
英文人名
Directive teacher:×××
Abstract：With the rapid development of information technology, natural language processing (NLP) has become a crucial research direction in the field of artificial intelligence. This study aims to explore new methods and applications in NLP based on machine learning to enhance the performance of tasks such as text understanding and semantic analysis. By comparing various classical machine learning algorithms, this research selects models suitable for NLP tasks and incorporates neural network structures from deep learning to optimize traditional algorithms, thereby constructing an efficient NLP fr amework. In the experimental section, large-scale corpora are used for training and testing, focusing on typical tasks including text classification, sentiment analysis, and named entity recognition. The results demonstrate that the proposed methods achieve significant improvements in accuracy and recall compared to traditional approaches. Innovatively, transfer learning is applied to NLP in low-resource scenarios, effectively alleviating data scarcity issues and providing new insights for addressing domain-specific NLP tasks. The primary contribution lies in integrating multiple machine learning techniques to form a comprehensive solution, promoting the advancement of NLP towards smarter and more efficient directions.
Keywords: Natural Language Processing；Machine Learning；Deep Learning；Text Classification；Transfer Learning
目 录
一、绪论 1
(一)研究背景与意义 1
(二)国内外研究现状 1
(三)本文研究方法与结构安排 1
二、机器学习算法在NLP中的应用 2
(一)监督学习的应用与挑战 2
(二)非监督学习的探索 2
(三)强化学习的作用与发展 3
三、自然语言处理关键技术分析 4
(一)词法分析技术进展 4
(二)句法语义解析方法 4
(三)文本生成技术研究 5
四、实际应用场景与案例分析 6
(一)智能客服系统实现 6
(二)机器翻译效果评估 7
(三)情感分析应用实例 7
结论 8
参考文献 8
致谢 9
关键词：自然语言处理；机器学习；深度学习；文本分类；迁移学习
Research on Natural Language Processing Based on Machine Learning
英文人名
Directive teacher:×××
Abstract：With the rapid development of information technology, natural language processing (NLP) has become a crucial research direction in the field of artificial intelligence. This study aims to explore new methods and applications in NLP based on machine learning to enhance the performance of tasks such as text understanding and semantic analysis. By comparing various classical machine learning algorithms, this research selects models suitable for NLP tasks and incorporates neural network structures from deep learning to optimize traditional algorithms, thereby constructing an efficient NLP fr amework. In the experimental section, large-scale corpora are used for training and testing, focusing on typical tasks including text classification, sentiment analysis, and named entity recognition. The results demonstrate that the proposed methods achieve significant improvements in accuracy and recall compared to traditional approaches. Innovatively, transfer learning is applied to NLP in low-resource scenarios, effectively alleviating data scarcity issues and providing new insights for addressing domain-specific NLP tasks. The primary contribution lies in integrating multiple machine learning techniques to form a comprehensive solution, promoting the advancement of NLP towards smarter and more efficient directions.
Keywords: Natural Language Processing；Machine Learning；Deep Learning；Text Classification；Transfer Learning
目 录
一、绪论 1
(一)研究背景与意义 1
(二)国内外研究现状 1
(三)本文研究方法与结构安排 1
二、机器学习算法在NLP中的应用 2
(一)监督学习的应用与挑战 2
(二)非监督学习的探索 2
(三)强化学习的作用与发展 3
三、自然语言处理关键技术分析 4
(一)词法分析技术进展 4
(二)句法语义解析方法 4
(三)文本生成技术研究 5
四、实际应用场景与案例分析 6
(一)智能客服系统实现 6
(二)机器翻译效果评估 7
(三)情感分析应用实例 7
结论 8
参考文献 8
致谢 9
