Language Modeling For Information Retrieval

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Language Modeling for Information Retrieval

W. Bruce Croft,John Lafferty
Language Modeling for Information Retrieval Book PDF

Author : W. Bruce Croft,John Lafferty
Publisher : Springer Science & Business Media
Page : 253 pages
File Size : 41,8 Mb
Release : 2013-04-17
Category : Computers
ISBN : 9789401701716

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Language Modeling for Information Retrieval by W. Bruce Croft,John Lafferty Pdf

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.

Statistical Language Models for Information Retrieval

ChengXiang Zhai
Statistical Language Models for Information Retrieval Book PDF

Author : ChengXiang Zhai
Publisher : Morgan & Claypool Publishers
Page : 142 pages
File Size : 48,7 Mb
Release : 2009
Category : Computers
ISBN : 9781598295900

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Statistical Language Models for Information Retrieval by ChengXiang Zhai Pdf

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

Statistical Language Models for Information Retrieval

Chengxiang Zhai
Statistical Language Models for Information Retrieval Book PDF

Author : Chengxiang Zhai
Publisher : Morgan & Claypool Publishers
Page : 141 pages
File Size : 46,9 Mb
Release : 2009-01-08
Category : Computers
ISBN : 9781598295917

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Statistical Language Models for Information Retrieval by Chengxiang Zhai Pdf

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

Introduction to Information Retrieval

Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Introduction to Information Retrieval Book PDF

Author : Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Publisher : Cambridge University Press
Page : 128 pages
File Size : 54,9 Mb
Release : 2008-07-07
Category : Computers
ISBN : 9781139472104

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Introduction to Information Retrieval by Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze Pdf

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.

Statistical Language Models for Information Retrieval

Chengxiang Zhai
Statistical Language Models for Information Retrieval Book PDF

Author : Chengxiang Zhai
Publisher : Springer Nature
Page : 132 pages
File Size : 49,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021305

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Statistical Language Models for Information Retrieval by Chengxiang Zhai Pdf

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

Dynamic Information Retrieval Modeling

Grace Hui Yang,Marc Sloan,Jun Wang
Dynamic Information Retrieval Modeling Book PDF

Author : Grace Hui Yang,Marc Sloan,Jun Wang
Publisher : Springer Nature
Page : 126 pages
File Size : 55,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031023019

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Dynamic Information Retrieval Modeling by Grace Hui Yang,Marc Sloan,Jun Wang Pdf

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.

Cross-Language Information Retrieval

Jian-Yun Nie
Cross Language Information Retrieval Book PDF

Author : Jian-Yun Nie
Publisher : Springer Nature
Page : 125 pages
File Size : 53,6 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021381

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Cross-Language Information Retrieval by Jian-Yun Nie Pdf

Search for information is no longer exclusively limited within the native language of the user, but is more and more extended to other languages. This gives rise to the problem of cross-language information retrieval (CLIR), whose goal is to find relevant information written in a different language to a query. In addition to the problems of monolingual information retrieval (IR), translation is the key problem in CLIR: one should translate either the query or the documents from a language to another. However, this translation problem is not identical to full-text machine translation (MT): the goal is not to produce a human-readable translation, but a translation suitable for finding relevant documents. Specific translation methods are thus required. The goal of this book is to provide a comprehensive description of the specific problems arising in CLIR, the solutions proposed in this area, as well as the remaining problems. The book starts with a general description of the monolingual IR and CLIR problems. Different classes of approaches to translation are then presented: approaches using an MT system, dictionary-based translation and approaches based on parallel and comparable corpora. In addition, the typical retrieval effectiveness using different approaches is compared. It will be shown that translation approaches specifically designed for CLIR can rival and outperform high-quality MT systems. Finally, the book offers a look into the future that draws a strong parallel between query expansion in monolingual IR and query translation in CLIR, suggesting that many approaches developed in monolingual IR can be adapted to CLIR. The book can be used as an introduction to CLIR. Advanced readers can also find more technical details and discussions about the remaining research challenges in the future. It is suitable to new researchers who intend to carry out research on CLIR. Table of Contents: Preface / Introduction / Using Manually Constructed Translation Systems and Resources for CLIR / Translation Based on Parallel and Comparable Corpora / Other Methods to Improve CLIR / A Look into the Future: Toward a Unified View of Monolingual IR and CLIR? / References / Author Biography

Next Generation Search Engines: Advanced Models for Information Retrieval

Jouis, Christophe
Next Generation Search Engines Advanced Models for Information Retrieval Book PDF

Author : Jouis, Christophe
Publisher : IGI Global
Page : 560 pages
File Size : 48,9 Mb
Release : 2012-03-31
Category : Computers
ISBN : 9781466603318

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Next Generation Search Engines: Advanced Models for Information Retrieval by Jouis, Christophe Pdf

Recent technological progress in computer science, Web technologies, and the constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Current search engines employ advanced techniques involving machine learning, social networks, and semantic analysis. Next Generation Search Engines: Advanced Models for Information Retrieval is intended for scientists and decision-makers who wish to gain working knowledge about search in order to evaluate available solutions and to dialogue with software and data providers. The book aims to provide readers with a better idea of the new trends in applied research.

Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition

Hang Li
Learning to Rank for Information Retrieval and Natural Language Processing Second Edition Book PDF

Author : Hang Li
Publisher : Springer Nature
Page : 107 pages
File Size : 50,7 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021558

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Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition by Hang Li Pdf

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

Information Retrieval Technology

Asia Information Retrieval Symposium
Information Retrieval Technology Book PDF

Author : Asia Information Retrieval Symposium
Publisher : Springer Science & Business Media
Page : 350 pages
File Size : 42,8 Mb
Release : 2005-02-23
Category : Computers
ISBN : 9783540250654

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Information Retrieval Technology by Asia Information Retrieval Symposium Pdf

This book constitutes the thoroughly refereed post-proceedings of the Asia Information Retrieval Symposium, AIRS 2004, held in Beijing, China, in October 2004. The 28 revised full papers presented have passed through two rounds of reviewing and improvement and were selected from 106 papers submitted. All current issues in information retrieval are addressed, ranging from algorithmic and methodological issues to application in various fields. Particular emphasis is given to aspects of Asian languages; text retrieval and Web information retrieval are addressed in several papers.

Natural Language Processing and Information Retrieval

Tanveer Siddiqui,U. S. Tiwary
Natural Language Processing and Information Retrieval Book PDF

Author : Tanveer Siddiqui,U. S. Tiwary
Publisher : Oxford University Press, USA
Page : 426 pages
File Size : 41,9 Mb
Release : 2008-05
Category : Computers
ISBN : UOM:39015080815528

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Natural Language Processing and Information Retrieval by Tanveer Siddiqui,U. S. Tiwary Pdf

Natural Language Processing and Information Retrieval is a textbook designed to meet the requirements of engineering students pursuing undergraduate and postgraduate programs in computer science and information technology. The book attempts to bridge the gap between theory and practice and would also serve as a useful reference for professionals and researchers working on language-related projects.

Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling

Chevalier, Max,Julien, Christine,Soule-Dupuy, Chantal
Collaborative and Social Information Retrieval and Access Techniques for Improved User Modeling Book PDF

Author : Chevalier, Max,Julien, Christine,Soule-Dupuy, Chantal
Publisher : IGI Global
Page : 390 pages
File Size : 45,5 Mb
Release : 2009-04-30
Category : Business & Economics
ISBN : 9781605663074

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Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling by Chevalier, Max,Julien, Christine,Soule-Dupuy, Chantal Pdf

"This book deals with the improvement of user modeling in the context of Collaborative and Social Information Access and Retrieval (CSIRA) techniques"--Provided by publisher.

Information Retrieval Technology

Akio Yamada,Helen Meng,Sung Hyon Myaeng
Information Retrieval Technology Book PDF

Author : Akio Yamada,Helen Meng,Sung Hyon Myaeng
Publisher : Springer
Page : 742 pages
File Size : 51,7 Mb
Release : 2005-11-15
Category : Computers
ISBN : 9783540320012

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Information Retrieval Technology by Akio Yamada,Helen Meng,Sung Hyon Myaeng Pdf

Asia Information Retrieval Symposium (AIRS) was established in 2004 by the Asian information retrieval community after the successful series of Information Retrieval with Asian Languages (IRAL) workshops held in six different locations in Asia, starting from 1996. The AIRS symposium aims to bring together international researchers and developers to exchange new ideas and the latest results in the field of information retrieval (IR). The scope of the symposium covers applications, systems, technologies and theoretical aspects of information retrieval in text, audio, image, video and multi-media data. We are very pleased to report that we saw a sharp and steady increase in the number of submissions and their qualities, compared with previous IRAL workshop series. We received 136 submissions from all over the world including Asia, North America, Europe, Australia, and even Africa, from which 32 papers (23%) were presented in oral sessions and 36 papers in poster sessions (26%). We also held a special session called “Digital Photo Albuming,” where 4 oral papers and 3 posters were presented. It was a great challenge and hard work for the program committee to select the best among the excellent papers. The high acceptance rates witness the success and stability of the AIRS series. All the papers and posters are included in this LNCS (Lecture Notes in Computer Science) proceedings volume, which is S- indexed. The technical program included two keynote talks by Prof. Walter Bender and Prof.

Machine Learning and Statistical Modeling Approaches to Image Retrieval

Yixin Chen,Jia Li,James Z. Wang
Machine Learning and Statistical Modeling Approaches to Image Retrieval Book PDF

Author : Yixin Chen,Jia Li,James Z. Wang
Publisher : Springer Science & Business Media
Page : 182 pages
File Size : 53,7 Mb
Release : 2006-04-11
Category : Science
ISBN : 9781402080357

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Machine Learning and Statistical Modeling Approaches to Image Retrieval by Yixin Chen,Jia Li,James Z. Wang Pdf

In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.

Advances in Information Retrieval

Craig Macdonald,Iadh Ounis,Vassilis Plachouras,Ian Ruthven,Ryan W. White
Advances in Information Retrieval Book PDF

Author : Craig Macdonald,Iadh Ounis,Vassilis Plachouras,Ian Ruthven,Ryan W. White
Publisher : Springer
Page : 722 pages
File Size : 40,9 Mb
Release : 2008-03-27
Category : Computers
ISBN : 9783540786467

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Advances in Information Retrieval by Craig Macdonald,Iadh Ounis,Vassilis Plachouras,Ian Ruthven,Ryan W. White Pdf

This book constitutes the refereed proceedings of the 30th annual European Conference on Information Retrieval Research, ECIR 2008, held in Glasgow, UK, in March/April 2008. The 33 revised full papers and 19 revised short papers presented together with the abstracts of 3 invited lectures and 32 poster papers were carefully reviewed and selected from 139 full article submissions. The papers are organized in topical sections on evaluation, Web IR, social media, cross-lingual information retrieval, theory, video, representation, wikipedia and e-books, as well as expert search.