基于预训练语言模型的中文短文本分类研究 毕业论文+项目源码及数据库文件
摘 要
该选题来源于当前自然语言处理领域中对于预训练语言模型和深度学习算法在实际应用场景――尤其是中文短文本分类任务中的前沿探索与需求。随着互联网信息爆炸式增长，准确快速地对海量中文短文本进行分类具有极高的实用价值和研究意义。
1. 预训练语言模型的发展为文本分类带来了全新的解决方案，如BERT等模型在理解语境和捕获深层次语义信息上表现出色，但在处理特定场景下的中文短文本时，可能需要进一步优化和调整。
2. CNN（卷积神经网络）在图像识别等领域取得了巨大成功，同样被广泛应用到文本分类任务中，然而原始CNN在处理短文本时可能会遇到特征提取不充分、模型复杂度过高影响运行效率等问题。
3. 选题旨在将预训练语言模型与经过改进的CNN算法相结合，以解决中文短文本分类中特征表达不足、分类效率低、精度待提升等关键问题，从而满足实际应用中对高效精准文本分类系统的迫切需求。
综上所述，本毕业设计选题源于学术界和工业界对提升中文短文本分类技术的研究热点和发展趋势，力求通过技术创新和实践验证，推动相关领域的理论研究和应用水平的进步。
本文针对中文短文本分类问题，提出了一种基于预训练语言模型并结合改进CNN算法的方法。首先，深入剖析了传统CNN在短文本分类任务中存在的特征提取局限性，创新性地引入了改进后的CHI方法以优化特征提取过程，使其更符合中文短文本特性和语义信息分布。其次，为了提升预训练语言模型与CNN结合的分类器运行效率，我们借鉴了Rocchio算法的思想以及其它高效策略，有效地提升了分类器的运行速度。再次，我们针对分类精度的优化，分别在相似度计算层面引进了基于属性熵值的相似度改进机制，以及基于CNN类别权重动态调整的方法，两者协同作用显著提高了分类精度。最终，基于上述多项改进措施，我们成功构建出一套高效、实用的适用于网站中文短文本分类的系统，实现了分类效果和处理速度的双重提升。
关键词：预训练语言模型、中文短文本分类、CNN算法、特征提取、CHI方法
Abstract
This topic comes from the frontier exploration and requirements of the pre-trained language model and deep learning algorithm in the practical application scenario ――, especially in the classification task of Chinese short text. With the explosive growth of Internet information, it is of high practical value and research significance to accurately and quickly classify massive Chinese short texts.
1. The development of pre-trained language models has brought new solutions to text classification. models such as BERT and other models perform well in understanding context and capturing deep semantic information, but may need further optimization and adjustment when processing Chinese short texts in specific scenarios.
2. CNN (convolutional neural network) has achieved great success in image recognition and other fields, and is also widely used in text classification tasks. However, the original CNN may encounter problems such as insufficient feature extraction, complex model and high impact operation efficiency.
3. The topic selection aims to combine the pre-trained language model with the improved CNN algorithm, so as to solve the key problems of insufficient classification, low efficiency of classification and accuracy to be improved, so as to meet the urgent needs of efficient and accurate text classification system in practical application.
To sum up, the topic of this graduation project originates from the research hotspot and development trend of academia and industry to improve the classification technology of Chinese short text, and strives to promote the progress of theoretical research and application level in related fields through technological innovation and practical verification.
This paper proposes a method based on pre-trained language model combined with improved CNN algorithm for the classification of Chinese short text. First, we deeply analyze the limitations of feature extraction of traditional CNN in short text classification task, and innovatively introduce the improved CHI method to optimize the feature extraction process and make it more in line with the distribution of Chinese short text. Secondly, in order to improve the operation efficiency of the classifier combined with the pre-trained language model and CNN, we borrowed the idea of Rocchio algorithm and other efficient strategies to effectively improve the operation speed of the classifier. Thirdly, for the optimization of classification accuracy, we introduced the similarity improvement mechanism based on the attribute entropy value and the dynamic adjustment of the CNN classification weight. The synergistic effect of the two significantly improved the classification accuracy. Finally, based on the above improvement measures, we successfully built a set of efficient and practical system suitable for the classification of Chinese short text on the website, which realized the double improvement of the classification effect and processing speed.
Key words: pre-training language model, Chinese short text classification, CNN algorithm, feature extraction, CHI method
目 录
1.1.2基于特征熵值分析的网站中文短文本分类系统的设计目标
5.3.3根据中文短文本的特征集与每类特征交集再次筛选候选类
5.5.3引入类别平均相似度改进在卷积神经网络中各类权重公式
