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摘要: 近年来，深度学习在图像和语音处理领域已经取得显著进展，但是在同属人类认知范畴的自然语言处理任务中，研究还未取得重大突破.本文首先从深度学习的应用动机、首要任务及基本框架等角度介绍了深度学习的基本概念；其次，围绕数据表示和学习模型两方面，重点分析讨论了当前面向自然语言处理的深度学习研究进展及其应用策略；并进一步介绍了已有的深度学习平台和工具；最后，对深度学习在自然语言处理领域的发展趋势和有待深入研究的难点进行了展望.Abstract: Recently, deep learning has made significant development in the fields of image and voice processing. However, there is no major breakthrough in natural language processing task which belongs to the same category of human cognition. In this paper, firstly the basic concepts of deep learning are introduced, such as application motivation, primary task and basic framework. Secondly, in terms of both data representation and learning model, this paper focuses on the current research progress and application strategies of deep learning for natural language processing, and further describes the current deep learning platforms and tools. Finally, the future development difficulties and suggestions for possible extensions are also discussed.
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图 9 几种常用的非线性化函数可视化表示
Fig. 9 Visual representation of several commonly used nonlinear functions
任务领域 原始输入 浅层特征 中间特征 高层特征 训练目标 语音 样本 频段 声音 音调 音素 – 单词 语音识别 图像 像素 线条 纹理 图案 局部 – 物体 图像识别 文本 字母 单词 词组 短语 句子 段落 文章 语义理解 
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模型 浅层模型 深层模型 理论 有成熟的理论基础 理论分析困难 模型层数 1~2层 5~10层 训练难度 容易 复杂, 需要较多技巧 数据需求 仅需要简单特征的任务, 如发电机故障诊断、时间序列处理等 需要高度抽象特征的任务, 如语音识别、图像处理等 模型表达能力 有限 强大 特征提取方式 特征工程 特征自动抽取 代价函数凸性 凸代价函数; 没有局部最优点; 可以收敛到全局最优 高度非凸的代价函数; 存在大量的局部最优点; 容易收敛到局部最优 先验知识依赖度 依赖更多先验知识 依赖较少先验知识
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