【摘要】 针对小样本学习标注训练样本过少，导致特征表达力弱的问题，本文结合有监督主题模型（Supervised LDA,SLDA）和动态路由算法提出一种新的动态路由原型网络模型（Dynamic routing prototypical network based on SLDA,DRP-SLDA）。利用SLDA主题模型建立词汇与类别之间的语义映射，增强词的类别分布特征，从词粒度角度编码获得样本的语义表示。提出动态路由原型网络（Dynamic routing prototypical network,DR-Proto），通过提取交叉特征利用样本之间的语义关系，采用动态路由算法迭代生成具有类别代表性的动态原型，旨在解决特征表达问题。实验结果表明，DRP-SLDA模型能有效提取词的类别分布特征，且获取动态原型提高类别辨识力，从而能够有效提升小样本文本分类的泛化性能。更多还原

【Abstract】 Aiming at the problem that the training samples for few-shot learning are too few,which leads to the weak expression of features,a novel dynamic routing prototypical network based on SLDA(DRP-SLDA) is proposed based on the supervised topic model(Supervised LDA, SLDA) and dynamic routing algorithm. The SLDA topic model is used to establish the semantic mapping between words and categories, enhance the category distribution characteristics of words, and obtain the semantic representation of samples from the perspective of word granularity. The dynamic routing prototypical network(DR-Proto)is presented. The network makes full use of the semantic relationship between samples by extracting cross features,and uses the dynamic routing algorithm to iteratively generate dynamic prototype with category representation, so as to solve the problem of feature expression. The experimental results show that the DRP-SLDA model can effectively extract the category distribution characteristics of words and dynamically obtain the dynamic prototype to increase the category identification, which can obviously improve the generalization ability of few-shot text classification.更多还原