Gobinda G. Chowdhury
Other affiliations: University of Technology, Sydney, Addis Ababa University, Nanyang Technological University ...read more
Bio: Gobinda G. Chowdhury is an academic researcher from Northumbria University. The author has contributed to research in topics: Digital library & Information system. The author has an hindex of 29, co-authored 81 publications receiving 3496 citations. Previous affiliations of Gobinda G. Chowdhury include University of Technology, Sydney & Addis Ababa University.
Papers published on a yearly basis
TL;DR: The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes.
Abstract: The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987-1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987-1991 and 1992-1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes.
TL;DR: This chapter presents the challenges of NLP, progress so far made in this field, NLP applications, components of N LP, and grammar of English language—the way machine requires it.
Abstract: Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems.
01 Jun 1999
TL;DR: It is this book's contention that it also benefits information professionals to learn the theory, techniques and tools that constitute the traditional approaches to the organization and processing of information.
Abstract: An information retrieval system is designed to analyse, process and store sources of information and retrieve those that match a particular user's requirements. A bewildering range of techniques is now available to the information professional attempting to achieve this goal. It is recognized that today's information professionals need to concentrate their efforts on learning the techniques of computerized information retrieval. However, it is this book's contention that it also benefits them to learn the theory, techniques and tools that constitute the traditional approaches to the organization and processing of information. In fact much of this knowledge may still be applicable in the storage and retrieval of electronic information in digital library environments.
01 Feb 2002
TL;DR: This book presents a holistic view of the new digital library scene, supported by a wealth of international examples, and is an essential guide to good digital practice and techniques.
Abstract: This book presents a holistic view of the new digital library scene. Supported by a wealth of international examples, it is an essential guide to good digital practice and techniques. The authors have experience both in teaching courses on digital libraries and in actively researching them, and the text is based on evidence provided by models of major digital library research projects around the globe. Key topics include: digital libraries: definition and characteristics; features of some digital libraries; digital library design; digital library research; collection management; digitization; information organization; information access and user interface; information retrieval in digital libraries; digital archiving and preservation; digital library services; social, economic and legal issues; digital library evaluation; digital libraries and the information profession; trends in digital library research and development.
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Abstract: Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.
TL;DR: Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
Abstract: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. © 2007 Wiley Periodicals, Inc.
21 Nov 2016
TL;DR: In this article, the authors constructed networks of collaboration between scientists in each of these disciplines and proposed a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number other scientists with whom they coauthored those papers.
Abstract: Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. Here we study a variety of nonlocal statistics for these networks, such as typical distances between scientists through the network, and measures of centrality such as closeness and betweenness. We further argue that simple networks such as these cannot capture variation in the strength of collaborative ties and propose a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers.
01 Jan 1993