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.     

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。     

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。     

质性知识构造与量化知识分析综论:兼论学术对标法理论基础

文章在简要综述知识基因、知识单元、知识表示和知识图谱的基础上,探讨知识的质性构造和量化分析基础,指出知识具有表型(外型)、构型(内型)和量型(量值)三型特征。用逻辑分析法从现象到本质揭示知识表型特征,包括语言文字型、公式符号型和图表图谱型;知识构型可用学科类别和特征概念标识;知识量型可基于数据或信息计算知识量。进一步提出用知识花图式和知识花程式展示质性知识构造与量化知识分析。由知识三型特征引出知识变换三假设以及学术评价"对标法";揭示质性知识构造适合解析静态知识特征,量化知识分析可延伸处理动态知识演化。质性知识构造与量化知识分析同时适用于剖析知识客体和知识主体,并独具学术评价应用价值。倡导通过知识分析创新图书情报学,同时也通过图书情报学促进知识之花盛开。     

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.     

基于机器学习的自动文本分类模型研究

基于机器学习的方法是自动文本分类中非常重要的一大类方法。本文先给出了形式化的定义，提出了自动文本分类的流程模型，然后选取了支持向量机（Support Vector Machine，SVM）算法作为一个典型例子进行分析，最后作者通过一个中文文本分类实验评价了该算法的效果。     

Analysis of Academic Libraries' Facebook Posts: Text and Data Analytics

This research analyzed a dataset of academic libraries' posts on Facebook. It applied a text and data analytics approach to a dataset collected from the Facebook posts of academic libraries at the top 100 English-speaking universities, as listed by the 2014 Shanghai World University Rankings. The dataset is from a two-year posting history of 18,333 unique posts, 113,621 likes, and 3401 comments. Less than a quarter of the libraries had more than 2000 post-related likes, and only seven received more than 100 comments on their postings. Content analysis identified the most prevalent single word (unigrams), bigrams (two-word sequences), and trigrams (three-word sequences) in high and low engagement content. Semantic analysis identified the semantic categories for posts with high and low engagement. The findings can assist academic libraries in their social media strategies for engagement, marketing, and visibility.     

Build It and They Will Come? Patron Engagement Via Twitter at Historically Black College and University Libraries

This study is a social media analysis on the use of Twitter at Historically Black Colleges and University (HBCU) libraries. While information science researchers have begun examining how libraries use social media, the vast majority of these studies are situated at large flagship research-intensive universities. Additionally, there currently exist deficiencies in research on social media deployment at HBCU libraries. We leverage, the IBM Watson's analytic engine, to systemically examine over 23,000, tweets over an eighteen-month period, around a set of objective measures including propagation of retweets and sentiment to assess follower engagement. The analysis found little evidence of follower engagement with library generated content. However, we observed a substantial volume of library tweets coalesced around institutional boosterism, rather than library related phenomena. This non-library related content represented the vast majority of retweets, but paradoxically was propagated by non-followers. Additionally, tweets relating to institutional boosterism produced the most positive sentiment within the data.     

The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions

Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions.