Statistical Score Calculation of Information Retrieval Systems using Data Fusion Technique

Abstract Effective information retrieval is defined as the number of relevant documents that are retrieved with respect to user query. In this paper, we present a novel data fusion in IR to enhance the performance of the retrieval system. The best data fusion technique that unite the retrieval results of nu merous systems using various data fusion algorith ms. The study show that our approach is more efficient than traditional approaches

Combining Textual and Visual Information for Image Retrieval in the Medical Domain

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP)

Analysing the temporal association among financial news using concept space model.

Law Yee-shan, Carol.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 81-89).Abstracts in English and Chinese.Chapter CHAPTER ONE --- INTRODUCTION --- p.1Chapter 1.1 --- Research Contributions --- p.5Chapter 1.2 --- Organization of the thesis --- p.5Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.7Chapter 2.1 --- Temporal data Association --- p.7Chapter 2.1.1 --- Association Rule Mining --- p.8Chapter 2.1.2 --- Sequential Patterns Mining --- p.10Chapter 2.2 --- Information Retrieval Techniques --- p.11Chapter 2.2.1 --- Vector Space model --- p.12Chapter 2.2.2 --- Probabilistic model --- p.75Chapter CHAPTER THREE --- AN OVERVIEW OF THE PROPOSED APPROACH --- p.16Chapter 3.1 --- The Test Bed --- p.19Chapter 3.2 --- General Concept Term Identification........................................……… --- p.19Chapter 3.3 --- Anchor Document Selection --- p.21Chapter 3.4 --- Specific Concept Term Identification --- p.22Chapter 3.5 --- Establishment of Associations --- p.22Chapter CHAPTER FOUR --- GENERAL CONCEPT TERM IDENTIFICATION --- p.24Chapter 4.1 --- Document Pre-processing --- p.25Chapter 4.2 --- Stopwording and stemming --- p.29Chapter 4.3 --- Word-phrase formation --- p.29Chapter 4.4 --- Automatic Indexing of Words and Sentences --- p.30Chapter 4.5 --- Relevance Weighting --- p.31Chapter 4.5.1 --- Term Frequency and Document Frequency Computation --- p.31Chapter 4.5.2 --- Uncommon Data Removal --- p.32Chapter 4.5.3 --- Combined Weight Computation --- p.32Chapter 4.5.4 --- Cluster Analysis --- p.33Chapter 4.6 --- Hopfield Network Classification --- p.35Chapter CHAPTER FIVE --- ANCHOR DOCUMENT SELECTION --- p.37Chapter 5.1 --- What is an anchor document? --- p.37Chapter 5.2 --- Selection Criteria of an anchor document --- p.40Chapter CHAPTER SIX --- DISCOVERY OF NEWS ASSOCIATION --- p.44Chapter 6.1 --- Specific Concept Term Identification --- p.44Chapter 6.2 --- Establishment of Associations --- p.45Chapter 6.2.1 --- Anchor document representation --- p.46Chapter 6.2.2 --- Similarity measurement --- p.47Chapter 6.2.3 --- Formation of a link of news --- p.48Chapter CHAPTER SEVEN --- EXPERIMENTAL RESULTS AND ANALYSIS --- p.54Chapter 7.1 --- Objective of Experiments --- p.54Chapter 7.2 --- Background of Subjects --- p.55Chapter 7.3 --- Design of Experiments --- p.55Chapter 7.3.1 --- Experimental Data --- p.55Chapter 7.3.2 --- Methodology --- p.55Anchor document selection --- p.57Specific concept term identification --- p.55News association --- p.59Chapter 7.4 --- Results and Analysis --- p.60Anchor document selection --- p.60Specific concept term identification --- p.64News association --- p.68Chapter CHAPTER EIGHT --- CONCLUSIONS AND FUTURE WORK --- p.72Chapter 8.1 --- Conclusions --- p.72Chapter 8.2 --- Future work --- p.74APPENDIX A --- p.76APPENDIX B --- p.78BIBLIOGRAPHY --- p.8