Information Retrieval Models
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Information Retrieval Models
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Author : Thomas Roelleke
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-07-01
Information Retrieval Models written by Thomas Roelleke and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-01 with Computers categories.
Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index
Information Retrieval
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Author : Ayse Goker
language : en
Publisher: John Wiley & Sons
Release Date : 2009-12-15
Information Retrieval written by Ayse Goker and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-15 with Technology & Engineering categories.
This book is an essential reference to cutting-edge issues and future directions in information retrieval Information retrieval (IR) can be defined as the process of representing, managing, searching, retrieving, and presenting information. Good IR involves understanding information needs and interests, developing an effective search technique, system, presentation, distribution and delivery. The increased use of the Web and wider availability of information in this environment led to the development of Web search engines. This change has brought fresh challenges to a wider variety of users’ needs, tasks, and types of information. Today, search engines are seen in enterprises, on laptops, in individual websites, in library catalogues, and elsewhere. Information Retrieval: Searching in the 21st Century focuses on core concepts, and current trends in the field. This book focuses on: Information Retrieval Models User-centred Evaluation of Information Retrieval Systems Multimedia Resource Discovery Image Users’ Needs and Searching Behaviour Web Information Retrieval Mobile Search Context and Information Retrieval Text Categorisation and Genre in Information Retrieval Semantic Search The Role of Natural Language Processing in Information Retrieval: Search for Meaning and Structure Cross-language Information Retrieval Performance Issues in Parallel Computing for Information Retrieval This book is an invaluable reference for graduate students on IR courses or courses in related disciplines (e.g. computer science, information science, human-computer interaction, and knowledge management), academic and industrial researchers, and industrial personnel tracking information search technology developments to understand the business implications. Intermediate-advanced level undergraduate students on IR or related courses will also find this text insightful. Chapters are supplemented with exercises to stimulate further thinking.
Statistical Language Models For Information Retrieval
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Author : Chengxiang Zhai
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009-01-08
Statistical Language Models For Information Retrieval written by Chengxiang Zhai and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-08 with Computers categories.
As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details. Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions
Experiment And Evaluation In Information Retrieval Models
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Author : K. Latha
language : en
Publisher: CRC Press
Release Date : 2017-07-28
Experiment And Evaluation In Information Retrieval Models written by K. Latha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Computers categories.
Experiment and Evaluation in Information Retrieval Models explores different algorithms for the application of evolutionary computation to the field of information retrieval (IR). As well as examining existing approaches to resolving some of the problems in this field, results obtained by researchers are critically evaluated in order to give readers a clear view of the topic. In addition, this book covers Algorithmic Solutions to the Problems in Advanced IR Concepts, including Feature Selection for Document Ranking, web page classification and recommendation, Facet Generation for Document Retrieval, Duplication Detection and seeker satisfaction in question answering community Portals. Written with students and researchers in the field on information retrieval in mind, this book is also a useful tool for researchers in the natural and social sciences interested in the latest developments in the fast-moving subject area. Key features: Focusing on recent topics in Information Retrieval research, Experiment and Evaluation in Information Retrieval Models explores the following topics in detail: Searching in social media Using semantic annotations Ranking documents based on Facets Evaluating IR systems offline and online The role of evolutionary computation in IR Document and term clustering, Image retrieval Design of user profiles for IR Web page classification and recommendation Relevance feedback approach for Document and image retrieval
Dynamic Information Retrieval Modeling
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Author : Grace Hui Yang
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Dynamic Information Retrieval Modeling written by Grace Hui Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. In this book we provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics. The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising. Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.
Information Representation And Retrieval In The Digital Age
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Author : Heting Chu
language : en
Publisher: Information Today, Inc.
Release Date : 2003
Information Representation And Retrieval In The Digital Age written by Heting Chu and has been published by Information Today, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Business & Economics categories.
Information representation and retrieval : an overview -- Information representation I : basic approaches -- Information representation II : other related topics -- Language in information representation and retrieval -- Retrieval techniques and query representation -- Retrieval approaches -- Information retrieval models -- Information retrieval systems -- Retrieval of information unique in content or format -- The user dimension in information representation and retrieval -- Evaluation of information representation and retrieval -- Artificial intelligence in information representation and retrieval.
Advances In Information Retrieval
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Author : W. Bruce Croft
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-11
Advances In Information Retrieval written by W. Bruce Croft and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-11 with Computers categories.
The Center for Intelligent Information Retrieval (CIIR) was formed in the Computer Science Department ofthe University ofMassachusetts, Amherst in 1992. The core support for the Center came from a National Science Foun- tion State/Industry/University Cooperative Research Center(S/IUCRC) grant, although there had been a sizeable information retrieval (IR) research group for over 10 years prior to that grant. Thebasic goal ofthese Centers is to combine basic research, applied research, and technology transfer. The CIIR has been successful in each of these areas, in that it has produced over 270 research papers, has been involved in many successful government and industry collaborations, and has had a significant role in high-visibility Internet sites and start-ups. As a result of these efforts, the CIIR has become known internationally as one of the leading research groups in the area of information retrieval. The CIIR focuses on research that results in more effective and efficient access and discovery in large, heterogeneous, distributed, text and multimedia databases. The scope of the work that is done in the CIIR is broad and goes significantly beyond “traditional” areas of information retrieval such as retrieval models, cross-lingual search, and automatic query expansion. The research includes both low-level systems issues such as the design of protocols and architectures for distributed search, as well as more human-centered topics such as user interface design, visualization and data mining with text, and multimedia retrieval.
A Feature Centric View Of Information Retrieval
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Author : Donald Metzler
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-09-18
A Feature Centric View Of Information Retrieval written by Donald Metzler and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-18 with Computers categories.
Commercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model. In addition, he describes several extensions, such as an automatic feature selection algorithm and a query expansion framework. The resulting model and extensions provide a flexible framework for highly effective retrieval across a wide range of tasks and data sets. A Feature-Centric View of Information Retrieval provides graduate students, as well as academic and industrial researchers in the fields of information retrieval and Web search with a modern perspective on information retrieval modeling and Web searches.
Information Retrieval Architecture And Algorithms
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Author : Gerald Kowalski
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-12-01
Information Retrieval Architecture And Algorithms written by Gerald Kowalski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-01 with Computers categories.
This text presents a theoretical and practical examination of the latest developments in Information Retrieval and their application to existing systems. By starting with a functional discussion of what is needed for an information system, the reader can grasp the scope of information retrieval problems and discover the tools to resolve them. The book takes a system approach to explore every functional processing step in a system from ingest of an item to be indexed to displaying results, showing how implementation decisions add to the information retrieval goal, and thus providing the user with the needed outcome, while minimizing their resources to obtain those results. The text stresses the current migration of information retrieval from just textual to multimedia, expounding upon multimedia search, retrieval and display, as well as classic and new textual techniques. It also introduces developments in hardware, and more importantly, search architectures, such as those introduced by Google, in order to approach scalability issues. About this textbook: A first course text for advanced level courses, providing a survey of information retrieval system theory and architecture, complete with challenging exercises Approaches information retrieval from a practical systems view in order for the reader to grasp both scope and solutions Features what is achievable using existing technologies and investigates what deficiencies warrant additional exploration
Introduction To Information Retrieval
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Author : Christopher D. Manning
language : en
Publisher: Cambridge University Press
Release Date : 2008-07-07
Introduction To Information Retrieval written by Christopher D. Manning and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-07 with Computers categories.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.