师专写作教改新探索

从师专学生自身的特点出发,从激发学生写作兴趣、营造开放的教学环境、重视写作练习设计、重视培养学生自己改的能力四个方面来改革教学方法,以达到提高学生写作能力的目的。     

初中英语课教法的尝试

本文通过对课文教学模式的转变,提高学生学习的积极性、增强培养学生的交际能力和创新能力.     

为学生写作插上翅膀

作文教学以学生为主体，注重个性发展倡导求异思维，让学生在学习中学会方法，得鱼而兼得渔。     

如何提高高职高专非英语专业学生的英语写作和翻译水平

分析了本校学生参加的云南省高校专科英语考试的成绩,认为非英语专业学生的写作和翻译能力低下的主要原因是学生对英语的重要性认识不足,学生的学习动机缺乏,学生的英语语法知识薄弱以及学生的词汇与短语的记忆不牢固。并论述了如何提高学生的这两方面的能力。     

Direct and indirect measures of writing for nonlearning disabled and learning disabled college students

Direct and indirect measures were used to compare the written language abilities of three groups of college students: two with learning disabilities and one without learning disabilities. Main effects were found for group, but not gender. Differences between nonlearning disabled students (NLD) and those with learning disabilities (LD) in writing were evident on both types of measures. Performance by LD students with disabilities in an area other than writing differed depending on the type of measure and often was no different from either of the other two groups. The combined use of direct and indirect measures appeared most effective for examining the complexities of writing produced by all groups.     

数据挖掘在数字图书馆中的应用

在描述数据挖掘技术与方法基础之上，探讨了数据挖掘在数字图书馆中的应用空间以及其所具有的巨大应用价值。     

英语学习者写作中搭配错误的个案研究

根据搭配的相关理论，选取英语专业学生的英语作文为语料，分析这些作文中搭配错误的特点及成因，并据此为教学提供建议。     

技术时代公共图书馆“场所”价值的社会公众认知研究——基于公共平台网络评论文本分析北大核心CSSCI

在技术时代,公共图书馆作为实体空间的"场所"价值,是关乎图书馆未来发展的重要内容。从公众认知角度探索图书馆"场所"价值,能够发现影响图书馆事业发展的重要因素。经过全面梳理和系统分析,选择上海图书馆、浦东图书馆和滨海新区图书馆三所代表性公共图书馆在公共平台上的网络评论信息,综合运用Python、Jieba分词、SnowNLP等数据采集与分析工具,从高频词分布、情感分析和时间序列三个维度,对社会公众认知情况进行了文本挖掘。分析结果表明,公共图书馆仍然是重要的公共阅读"场所";公共图书馆"场所"服务颇受欢迎,社会评价极为正面;促进文旅融合发展,是当前社会公众对公共图书馆"场所"价值认知的特征所在。在技术时代,公共图书馆应当高度重视"场所"价值的公众认知新变化,可以将文旅融合作为未来发展阶段的重要内容,助力公共图书馆高质量发展。     

基于统计频率的文本分类特征选择算法研究*

通过分析χ2统计量（Chi-square, CHI）的缺陷和不足,针对它对低文档频的特征项不可靠,而且不能说明词条和类别的相关性的缺点,对其进行改进,提出统计频率(Statistical Frequency, SF )算法。实验结果表明,统计频率算法能够弥补这些不足,在文本分类中表现出良好的分类效果。     

Two-stage statistical language models for text database selection

As the number and diversity of distributed Web databases on the Internet exponentially increase, it is difficult for user to know which databases are appropriate to search. Given database language models that describe the content of each database, database selection services can provide assistance in locating databases relevant to the information needs of users. In this paper, we propose a database selection approach based on statistical language modeling. The basic idea behind the approach is that, for databases that are categorized into a topic hierarchy, individual language models are estimated at different search stages, and then the databases are ranked by the similarity to the query according to the estimated language model. Two-stage smoothed language models are presented to circumvent inaccuracy due to word sparseness. Experimental results demonstrate that such a language modeling approach is competitive with current state-of-the-art database selection approaches.