Simple analysis of Arabic subject-indexing practice

Raises issues relating to Arabic subject-indexing based on an analysis of headings linked to a sample of 300 bibliographic records. Finds that, even in rapidly developing disciplines like IT, the high incidence of candidated new headings and of heading combinations may be influenced more by increased exposure during copy cataloguing to LCSH practice than by any acute need. Concludes that any real challenge to the usefulness to existing tools like El-Khazindar’s list of Arabic subject headings, published in 1994, will only come when libraries attempt to apply them systematically to all materials, Arabic and non-Arabic alike.     

The distribution of references across texts: Some implications for citation analysis

In citation network analysis, complex behavior is reduced to a simple edge, namely, node A cites node B. The implicit assumption is that A is giving credit to, or acknowledging, B. It is also the case that the contributions of all citations are treated equally, even though some citations appear multiply in a text and others appear only once. In this study, we apply text-mining algorithms to a relatively large dataset (866 information science articles containing 32,496 bibliographic references) to demonstrate the differential contributions made by references. We (1) look at the placement of citations across the different sections of a journal article, and (2) identify highly cited works using two different counting methods (CountOne and CountX). We find that (1) the most highly cited works appear in the Introduction and Literature Review sections of citing papers, and (2) the citation rankings produced by CountOne and CountX differ. That is to say, counting the number of times a bibliographic reference is cited in a paper rather than treating all references the same no matter how many times they are invoked in the citing article reveals the differential contributions made by the cited works to the citing paper.     

Charging mechanisms for digitised texts

This article is an edited and condensed version of the report of a study carried out under the auspices of the Joint Information Systems Committee (JISC) and the Publishers Association (PA). It develops the conclusions of an earlier analysis (Bide, Oppenheim & Ramsden, 1997) and builds upon discussions between representatives of JISC and the PA held in the summer of 1997. The full report is available on the UKOLN server at www.ukoln.ac.uk/services/elib/papers/pa/charging/     

Time gap analysis by the topic model-based temporal technique

This study proposes a temporal analysis method to utilize heterogeneous resources such as papers, patents, and web news articles in an integrated manner. We analyzed the time gap phenomena between three resources and two academic areas by conducting text mining-based content analysis. To this end, a topic modeling technique, Latent Dirichlet Allocation (LDA) was used to estimate the optimal time gaps among three resources (papers, patents, and web news articles) in two research domains. The contributions of this study are summarized as follows: firstly, we propose a new temporal analysis method to understand the content characteristics and trends of heterogeneous multiple resources in an integrated manner. We applied it to measure the exact time intervals between academic areas by understanding the time gap phenomena. The results of temporal analysis showed that the resources of the medical field had more up-to-date property than those of the computer field, and thus prompter disclosure to the public. Secondly, we adopted a power-law exponent measurement and content analysis to evaluate the proposed method. With the proposed method, we demonstrate how to analyze heterogeneous resources more precisely and comprehensively.     

A machine learning approach to sentiment analysis in multilingual Web texts

Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.     

A multi-collection latent topic model for federated search

Collection selection is a crucial function, central to the effectiveness and efficiency of a federated information retrieval system. A variety of solutions have been proposed for collection selection adapting proven techniques used in centralised retrieval. This paper defines a new approach to collection selection that models the topical distribution in each collection. We describe an extended version of latent Dirichlet allocation that uses a hierarchical hyperprior to enable the different topical distributions found in each collection to be modelled. Under the model, resources are ranked based on the topical relationship between query and collection. By modelling collections in a low dimensional topic space, we can implicitly smooth their term-based characterisation with appropriate terms from topically related samples, thereby dealing with the problem of missing vocabulary within the samples. An important advantage of adopting this hierarchical model over current approaches is that the model generalises well to unseen documents given small samples of each collection. The latent structure of each collection can therefore be estimated well despite imperfect information for each collection such as sampled documents obtained through query-based sampling. Experiments demonstrate that this new, fully integrated topical model is more robust than current state of the art collection selection algorithms.     

Incorporating web browsing activities into anchor texts for web search

Anchor texts complement Web page content and have been used extensively in commercial Web search engines. Existing methods for anchor text weighting rely on the hyperlink information which is created by page content editors. Since anchor texts are created to help user browse the Web, browsing behavior of Web users may also provide useful or complementary information for anchor text weighting. In this paper, we discuss the possibility and effectiveness of incorporating browsing activities of Web users into anchor texts for Web search. We first make an analysis on the effectiveness of anchor texts with browsing activities. And then we propose two new anchor models which incorporate browsing activities. To deal with the data sparseness problem of user-clicked anchor texts, two features of user’s browsing behavior are explored and analyzed. Based on these features, a smoothing method for the new anchor models is proposed. Experimental results show that by incorporating browsing activities the new anchor models outperform the state-of-art anchor models which use only the hyperlink information. This study demonstrates the benefits of Web browsing activities to affect anchor text weighting.     

Arabic literature: Uniterm indexing system for storage and retrieval

As centers of research and study in colleges and universities, Chinese academic libraries are heavily used and play a significant role in higher education. However, the level of administration can affect service to patrons and have an impact on their development. In this paper, the characteristics of administration in Chinese academic libraries are examined from both a historical perspective and current practice. The problems arising from the centralization of authority and administrative structure are also examined. Some possible suggestions are made of ways to improve library services in areas such as the quality of leadership, the creation of new positions, the increased responsibility of assistant directors, the re-organization of work distribution, the establishment of reference services, the development of faculty and staff participation in committee work, the importance of a library liaison program and the importance of designing a strategic plan for the library.     

创制亲近性文本:跨文化有效传播的重要基础

本文从精神文化传播的文本角度提出 ,创制“亲近性”文本是实现跨文化有效传播的重要基础。这种亲近性主要是指 :文本的表达方式与文化解读者期望的方式相一致 ,与其思维方式相一致 ,与其解读心理相一致。     

Picturing terrorism through Arabic lenses: a comparative analysis of Al Jazeera and Al Arabiya

This study examines the coverage of terrorism in two leading Arab news websites, Al Jazeera and Al Arabiya from 11 September 2009 to 10 September 2010. It finds that the stereotype that ‘the terrorist is a Muslim’ continues in terrorism coverage, despite the fact that some terrorists are non-Muslims. However, the two sites manage to send out the message that ‘the majority of terrorism victims are Muslims.’ In addition, the findings reveal that too much media focus is placed on disseminating and supporting official positions and decisions, and humanitarian sufferings from terrorism are seldom brought to the attention of the public.     

DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction

Topic emergence detection aids in pinpointing prominent topics within a given domain, providing practical insights into all interested parties on where to focus the limited resources. This paper employs the network-based topic evolution approach to overcome limitations in text-based topic evolution, providing prospective topic emergence prediction capabilities by representing emergent topics by their ancestors. A descendant-aware clustering algorithm is proposed to generate non-exhaustive and overlapping clusters, utilizing the pace of collaborations and structural similarities between topics with iterative edge removal and addition processes. Over 100 datasets specific to a research topic were extracted from the Microsoft Academic Graph dataset for the experiments, where the proposed algorithm consistently outperformed existing clustering algorithms in generating clusters with a higher likelihood of being ancestors to an emergent topic up to three years in the future. Regression-based cluster filtering using five structural cluster features and topic cluster qualities showed that the prediction performance can be enhanced by automatically classifying undesirable clusters from previously known data. The results showed that the proposed algorithm can enhance topic emergence predictions on a wide range of research domains regardless of their maturities, popularities, and magnitudes without having access to the data in the predicted year, paving a road to prospective predictions on emergent topics.     

新时期科技期刊编辑的选题意识

论述新时期科技期刊编辑树立选题意识的重要性,探讨选题过程中重点与一般、深度与广度、冷点与热点、继承与创新之间的相互关系。     

A machine learning approach for Arabic text classification using N-gram frequency statistics

In this paper a machine learning approach for classifying Arabic text documents is presented. To handle the high dimensionality of text documents, embeddings are used to map each document (instance) into R (the set of real numbers) representing the tri-gram frequency statistics profiles for a document. Classification is achieved by computing a dissimilarity measure, called the Manhattan distance, between the profile of the instance to be classified and the profiles of all the instances in the training set. The class (category) to which an instance (document) belongs is the one with the least computed Manhattan measure. The Dice similarity measure is used to compare the performance of method. Results show that tri-gram text classification using the Dice measure outperforms classification using the Manhattan measure.     

医学科普期刊专题策划的形式和方法

以《家庭医生》杂志为例,回顾、总结医学科普期刊做专题策划的形式和方法。认为专题系列和专题荟萃是专题策划2大主要表现形式,专题荟萃又可分为组合型和综合型。做专题策划的方法关键在于明确办刊宗旨,抓住热点,抓住需求。     

高校学报选题策划3原则

高校学报编辑开展选题策划有利于贯彻编辑方针和实现办刊宗旨，对提高学报质量有益。注重科学性与创新性、服务于读者和市场、求异(风格独特)求快(增强时效性)，是选题策划必须遵循的3条原则。     

Statistical representations from popular texts for the ordinary citizen, 1889–1914

Statistical representations in the popular almanacs published at the end of the 19th century in the Habsburg Empire are an early prototype of visualizing statistical data for popular consumption and informing the public of an ethnically and linguistically differentiated society. The purpose of this article is to analyze these statistical representations using a combination of humanistic and visual analysis methods. The article focuses on the persuasive aspects (rhetoric) of diagrams, how they visualize knowledge and information (pragmatics), and the nature and structure of visual sense making (semantics). The form and content are related to the social context and materiality of images. These early popular diagrams predate functional design defined by positivism and science. They are interpreted as a historical practice of knowledge visualization in the context of a post-Enlightenment information regime (epitomizing rational government and the privileged position of statistics as instrument of increasing the efficiency and social control of the state). The modernist approach shapes the same information regime in information science discourse. The analysis—based on principles of information visualization and functional design requirements—shows that these examples possess a sound visual structure based on functional design criteria, and that they integrate cultural context without distorting the data. The advantages of naturalistic and culturally rich visualizations that enable ordinary citizens to acquire knowledge through leveraging simple visual reasoning skills, reliance on mental models, and narrative conventions are identified.     

An analysis of the association between topic avoidance and dissatisfaction: comparing perceptual and interpersonal explanations

There is consistent evidence that individuals tend to be dissatisfied with their relationships if they report that topics are frequently avoided in those relationships. The extant literature contains two plausible general explanations for such findings: perceptual processes (e.g., the perceptions that the relational partner avoids are unrelated to the partner's actual avoidance but adversely influence relational satisfaction) and interpersonal processes (e.g., one individual's topic avoidance diminishes the counterpart's satisfaction because the counterpart accurately detects that avoidance). The current study compared the utility of these two explanations with data gathered from two separate samples, one including 100 heterosexual dating couples and the other including 114 parent-child dyads. Findings indicated that both perceptual processes and interpersonal processes help account for the association between topic avoidance and dissatisfaction. For example, the interpersonal explanation was consistent with evidence that boyfriends, girlfriends, and parents (but not children) all had perceptions of their counterpart's topic avoidance that were more accurate than would be expected by chance, and that those perceptions of avoidance were, in turn, associated with dissatisfaction. Despite such evidence for interpersonal factors, the effect sizes associated with perceptual processes were, in general, significantly stronger than those associated with interpersonal processes. The relative salience of the perceptual processes has important practical implications, supporting theoretical arguments that there are likely circumstances when topic avoidance can help maintain a cohesive relationship.