The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory.

中文翻译：

---

英语语法歧视训练网络模型和搜索过滤

基于统计的方法忽略了英语语法区域分支训练模型中的语义约束，无法有效识别方位信息。本文系统地讨论了英语语法领域分支训练模型过滤，英语语法领域分支训练模型检索与机器学习之间的紧密关系。通过分析情境在理解英语语法区分支训练模型中的作用，得出英语语法区分支训练模型与情境模型之间的关系以及英语语法区域分支训练模型与情境模型之间的相关性。确定，然后，提出了一套针对英语语法区域分支训练模型的过滤方法。目前，偏置滤波的研究很少，一般采用主题滤波的方法，效果较差。本文充分利用领域知识，采用语义模式分析技术，建立了丰富的语义分析资源，包括各种字典，规则，权重表示法，以有效地过滤倾斜的英语语法领域分支训练模型。语义数据源的引入解决了传统协作过滤系统中数据稀疏和冷启动的问题。另外，为了提高推荐系统的可扩展性和实时性，在离线数据预处理阶段，采用数据挖掘的方法对用户和项目进行模糊聚类。本文针对英语语法领域训练模型的方向提出了一种搜索过滤方案，对其细节进行了详细阐述，构造了从表示到权重的一整套功能结构，并给出了实验结果，证明该系统具有过滤效果好，速度快。与传统的统计方法相比，结果令人满意。