大学英语听力学习中发展预测和推理能力的策略

听力是大学英语学习中是十分重要的技能。预测、推理能力的短失成为不少学生进一步提高听力的障碍。本文就学习者如何在听力活动充分利用已学知识和其他有效学习策略发展自己的预测和推理能力做一讨论。     

基于ASP.NET的专家系统知识库设计

首先介绍了传统ASP中知识库的两种常用设计方法的优缺点及ASP .NET的技术特点 ,然后通过结合两种传统设计方法的优点 ,并结合基于ASP .NET的COM组件给出了构建基于ASP .NET的专家系统知识库的解决方案。     

Unified benchmark for zero-shot Turkish text classification

Effective learning schemes such as fine-tuning, zero-shot, and few-shot learning, have been widely used to obtain considerable performance with only a handful of annotated training data. In this paper, we presented a unified benchmark to facilitate the problem of zero-shot text classification in Turkish. For this purpose, we evaluated three methods, namely, Natural Language Inference, Next Sentence Prediction and our proposed model that is based on Masked Language Modeling and pre-trained word embeddings on nine Turkish datasets for three main categories: topic, sentiment, and emotion. We used pre-trained Turkish monolingual and multilingual transformer models which can be listed as BERT, ConvBERT, DistilBERT and mBERT. The results showed that ConvBERT with the NLI method yields the best results with 79% and outperforms previously used multilingual XLM-RoBERTa model by 19.6%. The study contributes to the literature using different and unattempted transformer models for Turkish and showing improvement of zero-shot text classification performance for monolingual models over multilingual models.     

逻辑分析在英语阅读理解中的应用

本文从逻辑的基本形式--概念、判断、推理出发,分析逻辑方法在英语阅读理解中的应用情况,通过实例说明其有效性及如何在阅读理解中应用概念、判断、推理,以便为更好的解答阅读理解提供方法上的突破.     

Semantic Web与基于语义的网络信息检索

本文描述网络环境语义检索的需求 ,分析SemanticWeb的组成框架 ,探讨概念集 (ontologies)及其定义和标记语言 ,并介绍基于概念集的信息资源语义标注和语义推理基本过程。     

试论关联理论与阅读理解

关联信息是指那些值得听话人关注的信息,关联理论在交际中有着非常重要的作用。本文试图从关联理论的角度出发,讨论阅读理解的认知过程,提出在教学中运用关联理论可以帮助学生提高阅读理解的准确性和有效性。     

The Impact of Over-Simplifying the Between-Subject Covariance Structure on Inferences of Fixed Effects in Modeling Nested Data

This study discusses the effects of oversimplifying the between-subject covariance structure on inferences for fixed effects in modeling nested data. Linear and quadratic growth curve models (GCMs) with both full and simplified between-subject covariance structures were fit to real longitudinal data. The results were contradictory to the statement that using oversimplified between-subject covariance structures (e.g., uni-level analysis) leads to underestimated standard errors of fixed effect estimates and thus inflated Type I error rates. We analytically derived simple mathematical forms to systematically examine the oversimplification effects for the linear GCMs. The derivation results were aligned with the real data analysis results and further revealed the conditions under which the standard errors of the fixed-effect intercept and slope estimates could be underestimated or overestimated for over-simplified linear GCMs. Therefore, our results showed that the underestimation statement is a myth and can be misleading. Implications are discussed and recommendations are provided.     

在数学分析教学中加强对学生综合能力的培养

从计算能力、读书能力、数学表达能力、逻辑思维和推理能力、综合分析与解决问题能力五个方面谈了如何加强对学生综合能力与素质的培养.     

关联理论的动态语境观与听力教学

大学生听力能力滞后有多方面的原因。语言水平低及背景情景知识的缺乏,可通过增加相应的输入进行弥补提高;而如何帮助学生在听辨过程中构建拓展相关语境假设进行有效语用推理,寻找最佳关联,则需要教师转换角色,与学生积极互动,帮助学生培养听力技巧及策略。     

基于ANFIS的实验教学质量评价的研究

本文分析了高等学校实验教学质量评价体系,阐述了自适应模糊推理系统(ANFIS)的算法和结构,并用于数控技术及应用课程实验教学质量评价,最后对评价结果作了分析。