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Introduction to Information Retrieval

Introduction to Information Retrieval Author Christopher D. Manning
ISBN-10 9781139472104
Release 2008-07-07
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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.

Introduction to Information Retrieval

Introduction to Information Retrieval Author Christopher D. Manning
ISBN-10 0521865719
Release 2008-07-07
Pages 482
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Class-tested and up-to-date textbook for introductory courses on information retrieval.

Introduction to Information Retrieval

Introduction to Information Retrieval Author Christopher D. Manning
ISBN-10 1107666392
Release 2017
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Introduction to Information Retrieval has been writing in one form or another for most of life. You can find so many inspiration from Introduction to Information Retrieval also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Introduction to Information Retrieval book for free.

Modern Information Retrieval

Modern Information Retrieval Author Ricardo Baeza-Yates
ISBN-10 0321416910
Release 2011
Pages 913
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This is a rigorous and complete textbook for a first course on information retrieval from the computer science perspective. It provides an up-to-date student oriented treatment of information retrieval including extensive coverage of new topics such as web retrieval, web crawling, open source search engines and user interfaces. From parsing to indexing, clustering to classification, retrieval to ranking, and user feedback to retrieval evaluation, all of the most important concepts are carefully introduced and exemplified. The contents and structure of the book have been carefully designed by the two main authors, with individual contributions coming from leading international authorities in the field, including Yoelle Maarek, Senior Director of Yahoo! Research Israel; Dulce Poncele´on IBM Research; and Malcolm Slaney, Yahoo Research USA. This completely reorganized, revised and enlarged second edition of Modern Information Retrieval contains many new chapters and double the number of pages and bibliographic references of the first edition, and a companion website with teaching material. It will prove invaluable to students, professors, researchers, practitioners, and scholars of this fascinating field of information retrieval.

Introduction to Modern Information Retrieval

Introduction to Modern Information Retrieval Author Gobinda G. Chowdhury
ISBN-10 9781856046947
Release 2010
Pages 508
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An information retrieval (IR) system is designed to analyse, process and store sources of information and retrieve those that match a particular user's requirements. A bewildering range of techniques is now available to the information professional attempting to successfully retrieve information. It is recognized that today's information professionals need to concentrate their efforts on learning the techniques of computerized IR. However, it is this book's contention that it also benefits them to learn the theory, techniques and tools that constitute the traditional approaches to the organization and processing of information. In fact much of this knowledge may still be applicable in the storage and retrieval of electronic information in digital library environments. The fully revised third edition of this highly regarded textbook has been thoroughly updated to incorporate major changes in this rapidly expanding field since the second edition in 2004, and a complete new chapter on citation indexing has been added. Unique in its scope, the book covers the whole spectrum of information storage and retrieval, including: users of IR and IR options; database technology; bibliographic formats; cataloguing and metadata; subject analysis and representation; automatic indexing and file organization; vocabulary control; abstracts and indexing; searching and retrieval; user-centred models of IR and user interfaces; evaluation of IR systems and evaluation experiments; online and CD-ROM IR; multimedia IR; hypertext and mark-up languages; web IR; intelligent IR; natural language processing and its applications in IR; citation analysis and IR; IR in digital libraries; and trends in IR research. Illustrated with many examples and comprehensively referenced for an international audience, this is an indispensable textbook for students of library and information studies. It is also an invaluable aid for information practitioners wishing to brush up on their skills and keep up to date with the latest techniques.

Text Data Management and Analysis

Text Data Management and Analysis Author ChengXiang Zhai
ISBN-10 9781970001174
Release 2016-06-30
Pages 530
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Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

Introduction to modern information retrieval

Introduction to modern information retrieval Author Gerard Salton
ISBN-10 0070544840
Release 1983
Pages 448
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Examines Concepts, Functions & Processes of Information Retrieval Systems

Information Retrieval

Information Retrieval Author Stefan Büttcher
ISBN-10 9780262528870
Release 2016-02-12
Pages 632
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An introduction to information retrieval, the foundation for modern search engines, that emphasizes implementation and experimentation.

Introduction to Information Retrieval and Quantum Mechanics

Introduction to Information Retrieval and Quantum Mechanics Author Massimo Melucci
ISBN-10 9783662483138
Release 2015-12-08
Pages 232
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This book introduces the quantum mechanical framework to information retrieval scientists seeking a new perspective on foundational problems. As such, it concentrates on the main notions of the quantum mechanical framework and describes an innovative range of concepts and tools for modeling information representation and retrieval processes. The book is divided into four chapters. Chapter 1 illustrates the main modeling concepts for information retrieval (including Boolean logic, vector spaces, probabilistic models, and machine-learning based approaches), which will be examined further in subsequent chapters. Next, chapter 2 briefly explains the main concepts of the quantum mechanical framework, focusing on approaches linked to information retrieval such as interference, superposition and entanglement. Chapter 3 then reviews the research conducted at the intersection between information retrieval and the quantum mechanical framework. The chapter is subdivided into a number of topics, and each description ends with a section suggesting the most important reference resources. Lastly, chapter 4 offers suggestions for future research, briefly outlining the most essential and promising research directions to fully leverage the quantum mechanical framework for effective and efficient information retrieval systems. This book is especially intended for researchers working in information retrieval, database systems and machine learning who want to acquire a clear picture of the potential offered by the quantum mechanical framework in their own research area. Above all, the book offers clear guidance on whether, why and when to effectively use the mathematical formalism and the concepts of the quantum mechanical framework to address various foundational issues in information retrieval.

Information Retrieval

Information Retrieval Author David A. Grossman
ISBN-10 0792382714
Release 1998-09-30
Pages 254
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Information Retrieval: Algorithms and Heuristics is a comprehensive introduction to the study of information retrieval covering both effectiveness and run-time performance. The focus of the presentation is on algorithms and heuristics used to find documents relevant to the user request and to find them fast. Through multiple examples, the most commonly used algorithms and heuristics needed are tackled. To facilitate understanding and applications, introductions to and discussions of computational linguistics, natural language processing, probability theory and library and computer science are provided. While this text focuses on algorithms and not on commercial product per se, the basic strategies used by many commercial products are described. Techniques that can be used to find information on the Web, as well as in other large information collections, are included. This volume is an invaluable resource for researchers, practitioners, and students working in information retrieval and databases. For instructors, a set of Powerpoint slides, including speaker notes, are available online from the authors.

Classification Made Simple

Classification Made Simple Author Eric J. Hunter
ISBN-10 9781351951043
Release 2017-03-02
Pages 176
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This established textbook introduces the essentials of classification as used for information processing. The third edition takes account of developments that have taken place since the second edition was published in 2002. Classification Made Simple provides a useful gateway to more advanced works and the study of specific schemes. As an introductory text, it will be invaluable to students of information work and to anyone inside or outside the information profession who needs to understand the manner in which classification can be utilized to facilitate and enhance organisation and retrieval.

Search Engines

Search Engines Author Bruce Croft
ISBN-10 9780133001594
Release 2011-11-21
Pages 552
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This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Search Engines: Information Retrieval in Practice is ideal for introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. It is also a valuable tool for search engine and information retrieval professionals. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice , is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book’s numerous programming exercises make extensive use of Galago, a Java-based open source search engine.

Web Information Retrieval

Web Information Retrieval Author Stefano Ceri
ISBN-10 9783642393143
Release 2013-08-30
Pages 284
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With the proliferation of huge amounts of (heterogeneous) data on the Web, the importance of information retrieval (IR) has grown considerably over the last few years. Big players in the computer industry, such as Google, Microsoft and Yahoo!, are the primary contributors of technology for fast access to Web-based information; and searching capabilities are now integrated into most information systems, ranging from business management software and customer relationship systems to social networks and mobile phone applications. Ceri and his co-authors aim at taking their readers from the foundations of modern information retrieval to the most advanced challenges of Web IR. To this end, their book is divided into three parts. The first part addresses the principles of IR and provides a systematic and compact description of basic information retrieval techniques (including binary, vector space and probabilistic models as well as natural language search processing) before focusing on its application to the Web. Part two addresses the foundational aspects of Web IR by discussing the general architecture of search engines (with a focus on the crawling and indexing processes), describing link analysis methods (specifically Page Rank and HITS), addressing recommendation and diversification, and finally presenting advertising in search (the main source of revenues for search engines). The third and final part describes advanced aspects of Web search, each chapter providing a self-contained, up-to-date survey on current Web research directions. Topics in this part include meta-search and multi-domain search, semantic search, search in the context of multimedia data, and crowd search. The book is ideally suited to courses on information retrieval, as it covers all Web-independent foundational aspects. Its presentation is self-contained and does not require prior background knowledge. It can also be used in the context of classic courses on data management, allowing the instructor to cover both structured and unstructured data in various formats. Its classroom use is facilitated by a set of slides, which can be downloaded from

Learning to Rank for Information Retrieval

Learning to Rank for Information Retrieval Author Tie-Yan Liu
ISBN-10 9781601982445
Release 2009
Pages 110
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Learning to Rank for Information Retrieval is an introduction to the field of learning to rank, a hot research topic in information retrieval and machine learning. It categorizes the state-of-the-art learning-to-rank algorithms into three approaches from a unified machine learning perspective, describes the loss functions and learning mechanisms in different approaches, reveals their relationships and differences, shows their empirical performances on real IR applications, and discusses their theoretical properties such as generalization ability. As a tutorial, Learning to Rank for Information Retrieval helps people find the answers to the following critical questions: To what respect are learning-to-rank algorithms similar and in which aspects do they differ? What are the strengths and weaknesses of each algorithm? Which learning-to-rank algorithm empirically performs the best? Is ranking a new machine learning problem? What are the unique theoretical issues for ranking as compared to classification and regression? Learning to Rank for Information Retrieval is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners.

Foundations of Statistical Natural Language Processing

Foundations of Statistical Natural Language Processing Author Christopher D. Manning
ISBN-10 0262133601
Release 1999
Pages 680
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An introduction to statistical natural language processing (NLP). The text contains the theory and algorithms needed for building NLP tools. Topics covered include: mathematical and linguistic foundations; statistical methods; collocation finding; word sense disambiguation; and probalistic parsing.

Visual Information Retrieval Using Java and LIRE

Visual Information Retrieval Using Java and LIRE Author Mathias Lux
ISBN-10 9781608459186
Release 2013
Pages 96
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Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) from large, unstructured repositories. The goal of VIR is to retrieve matches ranked by their relevance to a given query, which is often expressed as an example image and/or a series of keywords. During its early years (1995-2000), the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the lack of coincidence between an image's visual contents and its semantic interpretation, also known as semantic gap, required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this introductory book, we focus on a subset of VIR problems where the media consists of images, and the indexing and retrieval methods are based on the pixel contents of those images -- an approach known as content-based image retrieval (CBIR). We present an implementation-oriented overview of CBIR concepts, techniques, algorithms, and figures of merit. Most chapters are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for CBIR.

Current Challenges in Patent Information Retrieval

Current Challenges in Patent Information Retrieval Author Mihai Lupu
ISBN-10 9783662538173
Release 2017-04-19
Pages 455
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This second edition provides a systematic introduction to the work and views of the emerging patent-search research and innovation communities as well as an overview of what has been achieved and, perhaps even more importantly, of what remains to be achieved. It revises many of the contributions of the first edition and adds a significant number of new ones. The first part “Introduction to Patent Searching” includes two overview chapters on the peculiarities of patent searching and on contemporary search technology respectively, and thus sets the scene for the subsequent parts. The second part on “Evaluating Patent Retrieval” then begins with two chapters dedicated to patent evaluation campaigns, followed by two chapters discussing complementary issues from the perspective of patent searchers and from the perspective of related domains, notably legal search. “High Recall Search” includes four completely new chapters dealing with the issue of finding only the relevant documents in a reasonable time span. The last (and with six papers the largest) part on “Special Topics in Patent Information Retrieval” covers a large spectrum of research in the patent field, from classification and image processing to translation. Lastly, the book is completed by an outlook on open issues and future research. Several of the chapters have been jointly written by intellectual property and information retrieval experts. However, members of both communities with a background different to that of the primary author have reviewed the chapters, making the book accessible to both the patent search community and to the information retrieval research community. It also not only offers the latest findings for academic researchers, but is also a valuable resource for IP professionals wanting to learn about current IR approaches in the patent domain.