Multilingual modeling of cross-lingual spelling variants

Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV). To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns. We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64–78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.     

Author ranking evaluation at scale

We evaluate author impact indicators and ranking algorithms on two publication databases using large test data sets of well-established researchers. The test data consists of (1) ACM fellowship and (2) various life-time achievement awards. We also evaluate different approaches of dividing credit of papers among co-authors and analyse the impact of self-citations. Furthermore, we evaluate different graph normalisation approaches for when PageRank is computed on author citation graphs.We find that PageRank outperforms citation counts in identifying well-established researchers. This holds true when PageRank is computed on author citation graphs but also when PageRank is computed on paper graphs and paper scores are divided among co-authors. In general, the best results are obtained when co-authors receive an equal share of a paper's score, independent of which impact indicator is used to compute paper scores. The results also show that removing author self-citations improves the results of most ranking metrics. Lastly, we find that it is more important to personalise the PageRank algorithm appropriately on the paper level than deciding whether to include or exclude self-citations. However, on the author level, we find that author graph normalisation is more important than personalisation.     

A quantitative and model-driven approach to assessing higher education in the United States of America

University ranking or higher education assessment in general has been attracting more and more public attention over the years. However, the subjectivity-based evaluation index and indicator selections and weights that are widely adopted in most existing ranking systems have been called into question. In other words, the objectivity and impartiality of those rankings has been worrisome. To address these concerns, this paper presents a quantitative and model-driven approach to acquiring the evaluation index and indicator weights in the US News & World Report ranking system. Structural equation modelling will be applied to mine non-subjective weights from collected data. The proposed approach will be validated using two groups of United States universities, National Universities and Liberal Arts Colleges, classified by the US News & World Report. Managerial and administrative implications will also be explored. This study shows a very promising future because it opens a new venue for the scholars and practitioners in the higher education assessment field to develop a real-time, scalable and model-driven higher education ranking system.     

我国大学排行研究与实践的进展及评析

大学排行在传统上属于社会评估范畴。近年来,由于多主体的参与与高校改革,大学排行的传统概念与实践逐渐发生着变化。大学排行应该围绕“大学发展”的核心指标展开,构建大学发展性排行指标与评估体系。因为只有大学发展了,才能满足多方需求,更好地实现大学排行多种功能。     

Optimization of some factors affecting the performance of query expansion

This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed.     

Jointly valuing journal visibility and author citation count: An axiomatic approach

This paper proposes a simple, flexible, axiom-based mechanism for facilitating the comparison between a scholar's citation count and the visibility of the journals wherein the scholar's articles were published. The goal is to help research review bodies easily grasp the distinction these two forms of scholarly accomplishment and to provide a transparent way to articulate expectations about them to scholars. The approach is demonstrated using a widely applied and cooperative functional form that can reflect, via different parameter values, a wide range of possible beliefs about the relative merits of citation counts and journal visibility.     

论高等教育学生资助对象的认定

高等教育产品属于准公共产品,但各层次、各类别高校、各专业产品公共性是有区别的。个人高等教育选择依据其高等教育的私人产品性质而做出,在现有中国高等教育入学选拔制度下,最能反映高等教育私人产品性质的是各高校及专业选择性指标的高低,这可以从报考志愿拥挤度和录取分数得以反映。高等教育学生资助,首先应依据申请学生所在学校、专业的选择性指标对学生进行分类,实施以政府资助为主或是以市场、学校资助为主。其次应对贫困学生进行分级,学生学习生活每月消费支出（参考最低生活保障标准）、所学专业类型、家庭所在位置（城乡、区域）、学生交纳学费情况、家庭成员职业背景、家庭或成员特殊情况等指标最能反映经济困难程度,应根据这些指标计算出个体的贫困指数,对资助主体实施不同级别的资助。再次应完善学生资助对象认定路径,要加大非诚信者的机会成本、建立资助信息平台、完善市场化资助主体认定资助对象的相关政策、细化资助对象认定的具体措施。     

美国世界一流大学经费支出研究及启示

本文运用固定效应模型,分析近十年美国公立世界一流大学的经费支出数据。研究发现:美国一流公立大学中教学科研经费是主要支出;经费总支出与一流大学的排名没有显著相关性,但部分经费支出对大学排名有显著影响。对于无医院服务支出的高校,公共服务支出与大学排名显著正相关;对于有医院服务支出的高校,辅助经营支出的增加有助于大学排名的提升,公共服务支出的影响却相反。因此,提高经费使用效率比增加总额更为重要。我国“双一流”建设高校应该革新经费支出配置理念,建立科学有效的经费配置体系,增大学校各类服务支出。     

城镇居民住房获取概率的差异及其影响因素

基于城镇居民家庭特征变量和组织变量,采用 CGSS2013数据,通过 Logistic 模型分析我国城镇居民住房获取概率和获取不同类型住房概率的差异及其影响因素,研究表明：我国针对福利分房制度的市场化改革提高了居民的住房水平,但是在市场化转型过程中,再分配权力资源对住房资源仍然保持着占有上的优势。非市场因素对居民住房的获取产生了较为显著的影响,拥有制度优势的城镇户口居民、机关事业单位工作人员和商业精英在优质住房获取的机会上具有较为明显的优势。应继续深化以市场配置资源的住房制度改革,也要积极探索和推进户籍制度改革,消除“等级”观念的消极影响。     

我国大学排名研究综述

20世纪90年代以来,我国的大学排名活动开展得有声有色,在国内甚至国际有一定影响的大学排名已经有5家。大学排名活动已经成为我国高校评估领域一支不可忽视的力量。同时,对大学排名的研究工作也一直没有停止过。各种有关大学排名的研究文章也构成了高等教育研究中的一个小热点。对有关文章的各种观点从比较、作用、质疑和建议等四个方面进行理论梳理与归纳总结。