The Experts below are selected from a list of 37155 Experts worldwide ranked by ideXlab platform
Yannis Theoharis - One of the best experts on this subject based on the ideXlab platform.
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ECIR - Naming functions for the vector space model
Lecture Notes in Computer Science, 2007Co-Authors: Yannis Tzitzikas, Yannis TheoharisAbstract:The Vector Space Model (VSM) is probably the most widely used model for Retrieving Information from text collections (and recently from over other kinds of corpi). Assuming this model, we study the problem of finding the best query that "names" (or describes) a given (unordered or ordered) set of objects. We formulate several variations of this problem and we provide methods and algorithms for solving them.
A. Vina - One of the best experts on this subject based on the ideXlab platform.
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ISCC - Experiences Retrieving Information in the world wide web
Proceedings. Sixth IEEE Symposium on Computers and Communications, 1Co-Authors: Fidel Cacheda, A. VinaAbstract:In this paper using the Information obtained from the daily working of a Web directory, we attempt to expand the knowledge about the behavior of the users in order to improve and adapt the Internet search engines to their users. We have analysed more than 320,000 requests of the transaction log of a Spanish Web directory, focusing our attention, firstly, in the searches in order to confirm the main differences between Internet and traditional Information Retrieval systems. Furthermore, we have developed an exhaustive statistical analysis of searches, categories visited and documents viewed to achieve a mathematical pattern of behaviour for each one, and what it is more important, to establish a relationship between the variations in the behaviour of each one.
Dian Tjondronegoro - One of the best experts on this subject based on the ideXlab platform.
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ICDKE - Retrieving Information from Microblog Using Pattern Mining and Relevance Feedback
Data and Knowledge Engineering, 2012Co-Authors: Cher Han Lau, Xiaohui Tao, Dian TjondronegoroAbstract:Retrieving Information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing Information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.
Seongbok Baik - One of the best experts on this subject based on the ideXlab platform.
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Rethinking QR code: analog portal to digital world
Multimedia Tools and Applications, 2012Co-Authors: Seongbok BaikAbstract:In this paper, we are introducing a new view for the applications and the activities using QR codes to access the Information for the objects existing in everyday human environment. This view emphasizes the possibility of the QR codes as an Analog Portal—an ambient media gate to the Digital World, because it shows the new way of access to the Internet and may be able to change the culture of Retrieving Information when the QR code infrastructure becomes mature.
Yannis Tzitzikas - One of the best experts on this subject based on the ideXlab platform.
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ECIR - Naming functions for the vector space model
Lecture Notes in Computer Science, 2007Co-Authors: Yannis Tzitzikas, Yannis TheoharisAbstract:The Vector Space Model (VSM) is probably the most widely used model for Retrieving Information from text collections (and recently from over other kinds of corpi). Assuming this model, we study the problem of finding the best query that "names" (or describes) a given (unordered or ordered) set of objects. We formulate several variations of this problem and we provide methods and algorithms for solving them.
