Language Modeling For Information Retrieval
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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
Language Modeling For Information Retrieval
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Author : W. Bruce Croft
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Language Modeling For 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 2013-04-17 with Computers categories.
A statisticallanguage model, or more simply a language model, is a prob abilistic mechanism for generating text. Such adefinition is general enough to include an endless variety of schemes. However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative techniques to classify text into predefined cat egories. The first statisticallanguage modeler was Claude Shannon. In exploring the application of his newly founded theory of information to human language, Shannon considered language as a statistical source, and measured how weH simple n-gram models predicted or, equivalently, compressed natural text. To do this, he estimated the entropy of English through experiments with human subjects, and also estimated the cross-entropy of the n-gram models on natural 1 text. The ability of language models to be quantitatively evaluated in tbis way is one of their important virtues. Of course, estimating the true entropy of language is an elusive goal, aiming at many moving targets, since language is so varied and evolves so quickly. Yet fifty years after Shannon's study, language models remain, by all measures, far from the Shannon entropy liInit in terms of their predictive power. However, tbis has not kept them from being useful for a variety of text processing tasks, and moreover can be viewed as encouragement that there is still great room for improvement in statisticallanguage modeling.
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.
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.
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
Advances In Information Retrieval
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Author : Fabrizio Sebastiani
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-04-08
Advances In Information Retrieval written by Fabrizio Sebastiani 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 2003-04-08 with Computers categories.
This book constitutes the refereed proceedings of the 25th European Conference on Information Retrieval Research, ECIR 2003, held in Pisa, Italy, in April 2003. The 31 revised full papers and 16 short papers presented together with two invited papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on IR and the Web; retrieval of structured documents; collaborative filtering and text mining; text representation and natural language processing; formal models and language models for IR; machine learning and IR; text categorization; usability, interactivity, and visualization; and architectural issues and efficiency.
Information Retrieval
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Author : David A. Grossman
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-12
Information Retrieval written by David A. Grossman 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 2012-11-12 with Computers categories.
Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions. This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms. The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described. This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.
Multilingual Information Retrieval
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Author : Carol Peters
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-01-05
Multilingual Information Retrieval written by Carol Peters 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 2012-01-05 with Computers categories.
We are living in a multilingual world and the diversity in languages which are used to interact with information access systems has generated a wide variety of challenges to be addressed by computer and information scientists. The growing amount of non-English information accessible globally and the increased worldwide exposure of enterprises also necessitates the adaptation of Information Retrieval (IR) methods to new, multilingual settings. Peters, Braschler and Clough present a comprehensive description of the technologies involved in designing and developing systems for Multilingual Information Retrieval (MLIR). They provide readers with broad coverage of the various issues involved in creating systems to make accessible digitally stored materials regardless of the language(s) they are written in. Details on Cross-Language Information Retrieval (CLIR) are also covered that help readers to understand how to develop retrieval systems that cross language boundaries. Their work is divided into six chapters and accompanies the reader step-by-step through the various stages involved in building, using and evaluating MLIR systems. The book concludes with some examples of recent applications that utilise MLIR technologies. Some of the techniques described have recently started to appear in commercial search systems, while others have the potential to be part of future incarnations. The book is intended for graduate students, scholars, and practitioners with a basic understanding of classical text retrieval methods. It offers guidelines and information on all aspects that need to be taken into consideration when building MLIR systems, while avoiding too many ‘hands-on details’ that could rapidly become obsolete. Thus it bridges the gap between the material covered by most of the classical IR textbooks and the novel requirements related to the acquisition and dissemination of information in whatever language it is stored.
Dynamic Information Retrieval Modeling
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Author : Grace Hui Yang
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2016-06-01
Dynamic Information Retrieval Modeling written by Grace Hui Yang 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 2016-06-01 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 Retrieval Technology
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Author : Buren Zheng
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-16
Information Retrieval Technology written by Buren Zheng 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 2010-11-16 with Computers categories.
This book constitutes the refereed proceedings of the 6th Asia Information Retrieval Symposium, AIRS 2010, held in Taipei, Taiwan, in December 2010. The 26 revised full papers and 31 revised poster papers presented were carefully reviewed and selected from 120 submissions. All current aspects of information retrieval - in theory and practice - are addressed; the papers are organized in topical sections on information retrieval models, machine learning for information retrieval, user studies and evaluation, natural language processing for information retrieval, Web and question answering, and multimedia.
