摘 要
自然语言处理作为人工智能领域的重要分支，随着深度神经网络的发展迎来新的机遇与挑战。本研究旨在探索基于深度神经网络的自然语言处理技术，聚焦于提升文本理解与生成能力。针对传统方法在语义理解上的局限性，提出了一种融合多模态信息的深度学习框架，通过引入视觉、听觉等辅助信息增强模型对复杂语境的理解能力。实验采用大规模标注语料库进行训练与验证，结果显示该框架在语义角色标注、情感分析等多个任务上取得了显著优于现有方法的表现，特别是在长文本理解和跨领域迁移方面展现出独特优势。创新点在于首次将多模态感知机制系统性地应用于自然语言处理任务中，构建了从底层特征提取到高层语义解析的完整技术链条，为实现更加智能的人机交互提供了新思路。研究结果表明，所提出的架构不仅能够有效捕捉语言内部规律，还具备强大的泛化能力，对未来相关领域的研究具有重要参考价值。
关键词：自然语言处理；深度神经网络；多模态信息融合；语义理解；文本生成
Abstract
Natural Language Processing (NLP), as a crucial branch of artificial intelligence, has encountered new opportunities and challenges with the development of deep neural networks. This study aims to explore NLP techniques based on deep neural networks, focusing on enhancing text understanding and generation capabilities. Addressing the limitations of traditional methods in semantic understanding, we propose a multimodal information fusion deep learning fr amework that incorporates auxiliary information such as visual and auditory data to improve the model's comprehension of complex contexts. Experiments were conducted using large-scale annotated corpora for training and validation, demonstrating that this fr amework significantly outperforms existing methods in multiple tasks including semantic role labeling and sentiment analysis, particularly excelling in long text understanding and cross-domain transfer. The innovation lies in systematically applying multimodal perception mechanisms to NLP tasks for the first time, establishing a comprehensive technical chain from low-level feature extraction to high-level semantic parsing. The proposed architecture not only effectively captures internal language patterns but also exhibits strong generalization ability, providing new insights for achieving more intelligent human-computer interaction and offering significant reference value for future research in related fields.
Keywords：Natural Language Processing；Deep Neural Network；Multimodal Information Fusion；Semantic Understanding；Text Generation
目 录
摘 要 I
Abstract II
引 言 1
第一章 深度神经网络基础理论 2
1.1 深度学习基本概念 2
1.2 神经网络架构演变 2
1.3 关键算法原理分析 3
第二章 自然语言处理技术框架 5
2.1 语言模型构建方法 5
2.2 文本表示技术研究 5
2.3 序列标注任务实现 6
第三章 深度学习在NLP中的应用 8
3.1 机器翻译系统设计 8
3.2 情感分析模型构建 9
3.3 对话系统关键技术 9
第四章 当前挑战与未来方向 11
4.1 数据需求与获取 11
4.2 模型可解释性探索 11
4.3 技术发展趋势展望 12
结 论 14
参考文献 15
致 谢 16
