In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.

中文翻译：

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基于模糊计算和大数据的英语模糊文本智能识别与教学

在本文中，我们通过平行投影和区域扩展对英语模糊文本的智能识别和教学进行了深入的研究和分析。构建了基于非负矩阵分解和稀疏软聚类的多义词向量模型MSCVec。MSCVec 模型是一种单语词向量模型，它利用词与上下文之间正点互信息的非负矩阵分解来提取多义词混合语义的低秩表达，然后使用稀疏。它利用词和上下文之间的正逐点互信息的非负矩阵分解来提取多义词混合语义的低秩表达，然后使用稀疏软聚类算法对多义词的多个词义进行划分和还获得了多义词从属关系分布的全局意义；根据上下文语义和多义词之间全局从属关系的负平均对数似然确定具体的多义词簇类，最后在扩展词典词集下使用Fast文本模型学习多义词向量。MSCVec 模型的优势在于它是一个没有任何知识库的无监督学习过程，模型中的子串表示保证了未注册词向量的生成；此外，MSCVec 模型的全局从属关系也可以预期多语义词向量到单个词向量。与传统的静态词向量相比，MSCVec 在词相似度和下游文本分类任务实验中都表现出优异的结果。然后将两组特征融合并扩展为新的语义特征，并设计相似性分类实验和堆栈泛化实验进行比较。在跨语言句子级相似度检测任务中，SCLVec跨语言词向量词法级特征作为输入嵌入层优于MSCVec多义词向量特征；双循环神经网络训练的深层语义句子级特征优于双卷积神经网络的语义特征；对传统统计特征的扩展可以有效提高跨语言相似度检测性能，尤其是跨语言主题模型（BL-LDA）；堆栈泛化集成方法最大限度地提高了底层分类器的错误率，提高了检测精度。