An analysis of mobility in global rankings: making institutional strategic plans and positioning for building world-class universities

Since the start of the twenty-first century, university rankings have become internationalized. Global rankings have a variety of uses, levels of popularity and rationales and they are here to stay. An examination of the results of the current global ranking reveals that well-reputed world-class universities are amongst the top ranked ones. A major concern for university administrators in many parts of the world is how to use the global rankings wisely in their mid-term and long-term strategic planning for building their institutions into world-class universities. Four major global rankings have been developed: the Academic Ranking of World Universities, the World University Rankings, the Webometrics Ranking of World Universities and the Performance Ranking of Scientific Papers for World Universities. The main purpose of this paper is to explore the most influential indicators in these global university rankings that will affect the rank mobility of an institution. Based on an analysis of correlation coefficients and K-means clustering, a model of strategic institutional planning for building a world-class university is proposed.     

《美国新闻与世界报道》研究生院排行方法论的分析与启示

《美国新闻与世界报道》研究生院排行是大学排行的重要组成部分。研究生院排行的指标选择、数据来源方式以及统计方法都体现出科学性、可操作性以及与学院特色相适应的特点;我国研究生院排行由于起步较晚,排行方法论以借鉴为主,虽然注重本国研究生教育的特殊性,但在数据来源、指标选择、数据统计以及学院特色等方面相对于《美国新闻与世界报道》研究生院排行相对成熟的方法论体系仍存在较多问题。因此,在研究生院排行过程中要关注排行对象的特殊性与排行方法论选择的关系,以实现排行方法论体系本土化。     

企业声誉测评研究综述

企业声誉是企业持续竞争优势的重要来源,对其准确测评是全面判断企业声誉水平的前提,而科学的定义则是企业声誉测评的基础。本文试图通过整理现有国外相关研究文献,进一步明确企业声誉的内涵,并在此基础上,对目前国外声誉评价最具影响力的两种测评方法即提名法和排名法进行梳理,分析这两种方法的操作过程、优劣势和应用情况。本文发现,关注"情境"因素、注重消费者群体在声誉测评中的作用,将有助于企业声誉测评的改进,而开发适宜中小企业的声誉评价体系将更具现实意义。     

Improving educational web search for question-like queries through subject classification

Students use general web search engines as their primary source of research while trying to find answers to school-related questions. Although search engines are highly relevant for the general population, they may return results that are out of educational context. Another rising trend; social community question answering websites are the second choice for students who try to get answers from other peers online. We attempt discovering possible improvements in educational search by leveraging both of these information sources. For this purpose, we first implement a classifier for educational questions. This classifier is built by an ensemble method that employs several regular learning algorithms and retrieval based approaches that utilize external resources. We also build a query expander to facilitate classification. We further improve the classification using search engine results and obtain 83.5% accuracy. Although our work is entirely based on the Turkish language, the features could easily be mapped to other languages as well. In order to find out whether search engine ranking can be improved in the education domain using the classification model, we collect and label a set of query results retrieved from a general web search engine. We propose five ad-hoc methods to improve search ranking based on the idea that the query-document category relation is an indicator of relevance. We evaluate these methods for overall performance, varying query length and based on factoid and non-factoid queries. We show that some of the methods significantly improve the rankings in the education domain.     

Exploring criteria for successful query expansion in the genomic domain

Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome of these experiments, we discuss the success factors required for effective query expansion with respect to various sources of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline system.

Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering

Question answering (QA) aims at finding exact answers to a user’s question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT.     

科学搜索引擎Scirus的工作流程初探

随着信息技术的飞速发展和信息量的快速增长,人们面对的是浩如烟海的信息,如果没有搜索引擎的帮助,要想从中获得自己所需要的信息并非易事,特别是对于科学的、专业领域信息的搜索更是如此。Scirus正是迎合了这一信息需求,作为一个科学搜索引擎出现了。Scirus凭着优秀的搜索表现,连续三年被“搜索引擎观察”评为最佳专业搜索引擎。文章介绍了科学搜索引擎Scirus的倒金字塔结构的工作流程以及对“相似结果”的处理原理。     

万维网信息检索系统开发技术

当前搜索引擎技术有很大的局限性 ,本文首先分析了当前搜索引擎的若干缺陷 ,然后深入探讨在检索服务器端的若干处理技术 ,包括了Robot技术、索引技术、数据库技术、检索技术和相关度排序技术 ,同时还介绍了智能浏览器和智能体等客户端技术 ;最后文中还提出了综合运用这些技术的一个万维网信息智能检索系统的原型设想     

再论大学英语分级教学

实行分级教学应当是大学英语教学的核心,它体现了因地制宜、因材施教的教学原则和理念。但近年来由于人们对于四、六级考试成绩的误用,使得四、六级考试的通过率成了学校用来衡量教师教学能力,以及某些权威机构用来衡量学校教学质量的依据。也正是四、六级考试的这种指挥棒作用,使得分级教学成了许多学校力求提高他们四、六级通过率的一种手段,因而他们所采用的这种分级教学是否真的可取又成了人们争论的话题。     

课堂教学质量的区间值模糊评判

通过一元实函数和可信度的引入，给出了fuzzy区间数的一种排序方法，并就区间数fuzzy综合评判建立了一种课堂教学质量的综合评判模型．