基于词汇搭配的蒙古语兼类词语法和语义信息标注研究

通过对《蒙古文学语料库》中的兼类词文本进行语法和语义信息标注得出了以下结论:蒙古语兼类现象主要集中在形容词、名词、动词和副词之间;兼类词的语义分类信息最多为名词义类,其次为形容词义类、动词义类;兼类词的语义格信息最多为受格,其次是性状格、情态格。本实验对基于语义分类和语义格信息的蒙古语兼类词搭配的语义关系描述提供知识依据。

基于混合注意力机制的软件缺陷预测方法

软件缺陷预测技术用于定位软件中可能存在缺陷的代码模块,从而辅助开发人员进行测试与修复;传统的软件缺陷特征为基于软件规模、复杂度和语言特点等人工提取的静态度量元信息;然而,静态度量元特征无法直接捕捉程序上下文中的缺陷信息,从而影响了软件缺陷预测的性能;为了充分利用程序上下文中的语法语义信息,论文提出了一种基于混合注意力机制的软件缺陷预测方法 DP-MHA(defect prediction via mixed attention mechanism);DP-MHA首先从程序模块中提取基于AST树的语法语义序列并进行词嵌入编码和位置编码,然后基于多头注意力机制自学习上下文语法语义信息,最后利用全局注意力机制提取关键的语法语义特征,用于构建软件缺陷预测模型并识别存在潜在缺陷的代码模块;为了验证DP-MHA的有效性,论文选取了6个Apache的开源Java数据集,与经典的基于RF的静态度量元方法、基于RBM+RF、DBN+RF无监督学习方法和基于CNN和RNN深度学习方法进行对比,实验结果表明,DP-MHA在F1值分别提升了16.6%、34.3%、26.4%、7.1%、4.9%。

Extraction Algorithm of English Text Summarization for English Teaching

In order to improve the ability of sharing and scheduling capability of English teaching resources, an improved algorithm for English text summarization is proposed based on Association semantic rules. The relative features are mined among English text phrases and sentences, the semantic relevance analysis and feature extraction of keywords in English abstract are realized, the association rules differentiation for English text summarization is obtained based on information theory, related semantic roles information in English Teaching Texts is mined. Text similarity feature is taken as the maximum difference component of two semantic association rule vectors, and combining semantic similarity information, the accurate extraction of English text Abstract is realized. The simulation results show that the method can extract the text summarization accurately, it has better convergence and precision performance in the extraction process.

Research on Application of Data Mining in Virtual Community of Foreign Language Learning

The construction of virtual community in foreign language learning is a comprehensive foreign language learning environment integrated with foreign language vocabulary database construction and vocabulary retrieval, combining the virtual reality technology to construct the language environment of foreign language learning. The virtual community of foreign language leaming can improve the sense of language authenticity in foreign language learning and improve the quality of foreign language teaching. A method of building a virtual community for foreign language learning is proposed based on data mining technology, data acquisition and feature preprocessing model for building semantic vocabulary of foreign language learning is constructed, the linguistic environment characteristics of the semantic vocabulary data of foreign language learning is analyzed, and the semantic noumenon structure model is obtained. Fuzzy clustering method is used for vocabulary clustering and comprehensive retrieval in the virtual community of foreign language learning, the performance of vocabulary classification in foreign language learning is improved, the adaptive semantic information fusion method is used to realize the vocabulary data mining in the virtual community of foreign language learning, information retrieval and access scheduling for virtual communities in foreign language learning are realized based on data mining results. The simulation results show that the accuracy of foreign language vocabulary retrieval is good, improve the efficiency of foreign language learning.