Multilingual Mix in Hong Kong Advertising,Pre- and Post-1997

The paper examines the historical change in linguistic practice of Hong Kong advertising through the decolonization period and attempts to make sense of it from a local-and-global point of view beyond the conventional national-functionalist perspective. It is found that the prominent patterns of language mixing in the advertising of Hong Kong are not only ‘Standard Written Chinese mixed with English’, but also ‘Standard Written Chinese mixed with both English and Cantonese’. The embedded elements of both English and Cantonese in the Chinese advertisements can take the form of a word, a phrase and/or a full clause and can serve both informational and involving functions. Finally, it is argued that multilingual mix in Hong Kong advertising is a phenomenon constantly emerging in the multicultural process of globalization and that being able to mix different languages or varieties of languages for effective communication should be an aspect of linguistic competence highly valued in the age of globalization and localization.     

Multilingual Digital Libraries: A review of issues in system-centered and user-centered studies,information retrieval and user behavior

Abstract

This study aims to identify, collect and critical review the research literature on Multilingual Digital Libraries in English language from 1997 to 2012.

Design/methodology/approach: The present literature review has followed the rules of systematic review. In particular, the identified relevant papers were categorized based on their expressed aim on two core themes, that of system-centered and user-centered studies. The assigned papers were further analyzed and six sub-themes emerged for the system-centered studies and four for the user-centered studies. Additional categorization was also provided according to type of publication.

Findings: The literature concerning Multilingual Digital Libraries is vast and mainly focuses on two aspects the “System” and the “Users”. The majority of papers tried to meet the challenges raised for enabling multilingual information retrieval in Digital Libraries. Unfortunately, these efforts undertaken by a small number of researchers or research groups apparently working in isolation and therefore resulting in the development of numerous different tools and techniques. Relatively few studies have focused on the user and aimed to explore users' behavior and expectations when interacting with Multilingual Digital Libraries. As a result, further research is needed to reach to some tangible and usable findings.

Originality/value: This literature review captures the diversity of the research conducted regarding multilingual information access and retrieval in Digital Libraries. It organizes the vast literature in comprehensive themes and sub-themes enabling easy access to specific information.

Limitations: This study reviews only papers in English due to language restrictions from 1997 to 2012.     

数字图书馆的多语言信息存取——Europeana项目进展与启示

介绍欧洲数字图书馆项目Europeana的多语言存取研究进展,包括拟实现的功能、内部架构和关键技术,如语义数据层的词表映射、命名实体识别、翻译消歧、语言识别等,并分析了数字图书馆的多语言存取存在的问题和可能解决的方法,为我国数字图书馆建设提供借鉴。     

多语言数字图书馆信息生态链的结构、类型及启示

文章基于信息生态学理论与方法,通过分析多语言数字图书馆信息生态链的结构模型与组合类型,总结多语言数字图书馆信息生态链的多样性特征,为我国多语言数字图书馆建设提出若干政策建议,以期推动我国多语言数字图书馆建设和管理的科学化进程。     

Text Analysis of Communication Faculty Publications to Identify Research Trends and Interest

Text analysis has been widely used to identify research trends in many disciplines. However, little has been written about using this method to discover a department’s research trends and faculty research interests in the library setting. This study examined 107 faculty publications from the School of Communication at the University of Houston. It analyzed word and phrase frequencies from titles and abstracts using Voyant tools. The analysis was performed on both the department level and the individual faculty level. This article demonstrates a new way for librarians to understand faculty research. The method can be replicated by subject librarians to analyze their own departments.     

面向概念挖掘的文本层次模型研究

针对当前Web文本挖掘工具的不足之处,提出了一种基于层次结构、面向概念挖掘的模型,即文本层次模型。该模型具有数据源适应性强、结构灵活、可操作性强、用途广泛优点,具有很强的实用性和一定的可扩展能力。图2。参考文献13。     

判断新兴研究趋势的技术框架研究

目前,新兴研究趋势的探测成为国内外的研究热点。本文分析了新兴研究趋势的概念以及国外在新兴研究趋势探测上所做的相关工作,并分析了当前研究存在的一些问题。同时结合对新兴研究趋势表现特点的总结,笔者提出从海量的信息资源中判断新兴研究趋势的技术框架,并以医学信息资源为数据来源,综合采用文本挖掘技术、文献计量学、网络计量学、时间序列分析等方法,来完成主题领域界定、主题特征表示以及最终的综合判断。     

Topology-driven trend analysis for drug discovery

The primary goal of the present study is to discover new drug treatments by topology analysis of drug associations and their therapeutic group network. To this end, we collected 19,869 papers dated from 1946 to 2015 that are related to autism treatment from PubMed. We extracted 145 drugs based on MeSH terms and their synonyms (the total number is 6624) within the same ATC classification hierarchy and used them to find drug associations in the collected datasets. We introduced a new topology-driven method that incorporates various network analyses including co-word network, clique percolation, weak component, pathfinding-based analysis of therapeutic groups, and detection of important drug interaction within a clique. The present study showed that the in-depth analysis of the drug relationships extracted from the literature-based network sheds new light on drug discovery research. The results also suggested that certain drugs could be repurposed for autism treatment in the future. In particular, the results indicated that the discovered four drugs such as Tocilizumab, Tacrolimus, Prednisone, and Sulfisoxazole are worthy of further study in laboratory experiments with formal assessment of possible effects on symptoms, which may provide psychologists, physicians, and researchers with data-based scientific hypotheses in autism-drug discovery.     

Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method’s ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities.     

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.     

基于Web文本挖掘的行业态势分析——以2011上海车展为例

通过总结Web文本挖掘在竞争情报中的应用,尝试将Web文本挖掘方法应用于行业态势分析,提出基于行业态势分析的Web文本挖掘流程,并以2011年上海车展中的相关报道,采用时空分布、词频分析、共现分析等方法对汽车行业态势分析进行实证研究,最后探讨我国汽车行业的发展趋势。     

Deriving technology intelligence from patents: Preposition-based semantic analysis

Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents.     

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.     

专利技术形态分析方法研究进展

专利信息分析方法在揭示专利技术信息方面已取得许多研究成果,形态分析是一种面向未来的系统分析方法,专利技术形态分析方法可以综合这两种方法的优点,为面向未来的技术分析提供一个重要研究方向。对专利技术形态分析的核心方法与研究进展进行系统介绍,并对该方法目前有待研究的问题及其解决思路以及未来的应用进行分析。     

基于文本挖掘的不同购物网站商品评论一致性研究

基于文本挖掘的理论,提出不同购物网站商品评论对比分析的方法,对不同购物网站同一商品评论是否一致进行研究。首先对商品单个特征的评论进行对比分析,然后衍生到商品的整体特征对比。研究发现,不同购物网站对同一商品的评论并不完全一致,这种不一致主要体现在商品特征上面,这说明商品评论会因为购物网站的不同而有所差异。     

图情领域中专利分析主题的研究进展——基于WOS的文献分析

为研究专利分析方法、技术以及应用等方面的最新进展,采用文献计量法,对以专利分析为主题的文献进行分析,总结国外专利分析主题的研究进展情况。利用汤森路透WOS(web of science)引文检索平台,检索 2000-2012年以"专利分析"为主题的文献,通过主题分析(高频关键词)以及关键词共现分析,得到国外专利分析相关文献的主题。研究发现,国外近10年以专利分析为主题的文献主要围绕专利计量法、专利语义分析、社会网络分析与模型、文本挖掘、可视化以及科学-技术关联等方面,并在此基础上展开了新的研究。     

共享住宿与酒店用户评论文本的跨平台比较研究:基于LDA的主题社会网络和情感分析

[目的/意义]共享住宿与酒店预定平台可能同时存在替代性和互补性,但这种替代性和互补性分别体现在哪些产品和服务上当前文献还缺乏探讨,需要进一步开展跨平台的比较研究.[方法/过程]选取携程酒店预定平台和小猪短租平台为实验对象,采集北京市相关房源的86 635条用户评论文本,结合LDA模型、主题社会网络和主题情感分析方法对用...     

数据挖掘技术在高校图书馆中的应用

数据挖掘技术是一种新兴的信息处理技术,其算法及应用是目前国际研究热点,在信息的利用和提取中发挥着日益重要的作用.文章在论述数据挖掘技术的基础上,探讨了数据挖掘在高校图书馆的应用领域和产生的价值.并重点探讨了在优化资源、智能化服务、提供个性化服务、信息自动化处理等几方面的应用.     

面向信息分析的专利术语抽取研究

从信息分析的实际需求出发,对与电动汽车相关的5 405条专利数据进行术语抽取、生僻术语识别和字段比较研究。结果显示关键短语抽取的方法可行,互信息抽取的术语所在文档的平均文档长度更接近集合的平均文档长度;摘要和First Claim字段的术语存在一定差别,但对分类或聚类同等重要;生僻术语识别算法能够发现生僻词和高频词的对应关系。研究结论可以为专利文本挖掘和专利信息分析提供结果和方法,并为信息分析工作提供所需的参考术语。     

国外专利文本挖掘可视化工具研究

首先简要介绍专利信息分析概念、专利分析的一般流程,专利分析工具可实现的主要功能;其次依据专利分析工具可分析的数据源,将分析工具分为非结构化数据分析工具、结构化数据分析工具和混合型数据分析工具三大类,并从分析工具类型、分析数据源、主要功能、结果呈现、用户群5个方面对国外常用的12种专利文本挖掘可视化分析工具进行系统介绍和比较;最后对专利分析工具应用及其发展提出建议。