Author: Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Publisher: Cambridge University Press
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.
Author: Gerard Salton,Michael J. McGill
Publisher: McGraw-Hill College
Examines Concepts, Functions & Processes of Information Retrieval Systems
Author: Gobinda G. Chowdhury
Publisher: Library Assn Pub Limited
Accepting that information professionals need to learn both traditional and computerized information retrieval techniques, this book aims to provide students with a comprehensive view of information retrieval. It covers: classification, cataloguing, subject indexing, abstracting and vocabulary control; CD-ROM and online information retrieval, multimedia; hypertext and hypermedia; expert systems and natural language processing techniques; knowledge-based natural language, text processing and user interface systems; and information retrieval in the context of the Internet, the World Wide Web, and the digital library environment.
Author: G. G. CHOWDHURY
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.
Author: Massimo Melucci
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.
an introduction to information retrieval
Author: J. E. Rowley
Author: Tie-Yan Liu
Publisher: Springer Science & Business Media
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.
Publisher: "Издательство ""Проспект"""
Ausfertigungsdatum: 15.05.1871 Vollzitat: "Strafgesetzbuch in der Fassung der Bekanntmachung vom 13. November 1998 (BGBl. I S. 3322), das durch Artikel 1 des Gesetzes vom 24. September 2013 (BGBl. I S. 3671) geändert worden ist" Stand: Neugefasst durch Bek. v. 13.11.1998 I 3322; Zuletzt geändert durch Art. 23 G v. 20.11.2015 I 2010 Hinweis: Änderung durch Art. 1 G v. 20.11.2015 I 2025 (Nr. 46) noch nicht berücksichtigt!
Theory and Implementation
Author: Gerald J. Kowalski
The growth of the Internet and the availability of enormous volumes of data in digital form have necessitated intense interest in techniques to assist the user in locating data of interest. The Internet has over 350 million pages of data and is expected to reach over one billion pages by the year 2000. Buried on the Internet are both valuable nuggets to answer questions as well as a large quantity of information the average person does not care about. The Digital Library effort is also progressing, with the goal of migrating from the traditional book environment to a digital library environment. The challenge to both authors of new publications that will reside on this information domain and developers of systems to locate information is to provide the information and capabilities to sort out the non-relevant items from those desired by the consumer. In effect, as we proceed down this path, it will be the computer that determines what we see versus the human being. The days of going to a library and browsing the new book shelf are being replaced by electronic searching the Internet or the library catalogs. Whatever the search engines return will constrain our knowledge of what information is available. An understanding of Information Retrieval Systems puts this new environment into perspective for both the creator of documents and the consumer trying to locate information.
Management, Types, and Standards
Author: Marcia J. Bates
Publisher: CRC Press
Category: Business & Economics
In order to be effective for their users, information retrieval (IR) systems should be adapted to the specific needs of particular environments. The huge and growing array of types of information retrieval systems in use today is on display in Understanding Information Retrieval Systems: Management, Types, and Standards, which addresses over 20 types of IR systems. These various system types, in turn, present both technical and management challenges, which are also addressed in this volume. In order to be interoperable in a networked environment, IR systems must be able to use various types of technical standards, a number of which are described in this book—often by their original developers. The book covers the full context of operational IR systems, addressing not only the systems themselves but also human user search behaviors, user-centered design, and management and policy issues. In addition to theory and practice of IR system design, the book covers Web standards and protocols, the Semantic Web, XML information retrieval, Web social mining, search engine optimization, specialized museum and library online access, records compliance and risk management, information storage technology, geographic information systems, and data transmission protocols. Emphasis is given to information systems that operate on relatively unstructured data, such as text, images, and music. The book is organized into four parts: Part I supplies a broad-level introduction to information systems and information retrieval systems Part II examines key management issues and elaborates on the decision process around likely information system solutions Part III illustrates the range of information retrieval systems in use today discussing the technical, operational, and administrative issues for each type Part IV discusses the most important organizational and technical standards needed for successful information retrieval This volume brings together authoritative articles on the different types of information systems and how to manage real-world demands such as digital asset management, network management, digital content licensing, data quality, and information system failures. It explains how to design systems to address human characteristics and considers key policy and ethical issues such as piracy and preservation. Focusing on web–based systems, the chapters in this book provide an excellent starting point for developing and managing your own IR systems.
Author: Efthimis Efthimiadis,Juan M. Fernández-Luna,Juan F. Huete,Andrew MacFarlane
Publisher: Springer Science & Business Media
Information Retrieval has become a very active research field in the 21st century. Many from academia and industry present their innovations in the field in a wide variety of conferences and journals. Companies transfer this new knowledge directly to the general public via services such as web search engines in order to improve their information seeking experience. In parallel, teaching IR is turning into an important aspect of IR generally, not only because it is necessary to impart effective search techniques to make the most of the IR tools available, but also because we must provide a good foundation for those students who will become the driving force of future IR technologies. There are very few resources for teaching and learning in IR, the major problem which this book is designed to solve. The objective is to provide ideas and practical experience of teaching and learning IR, for those whose job requires them to teach in one form or another, and where delivering IR courses is a major part of their working lives. In this context of providing a higher profile for teaching and learning as applied to IR, the co-editor of this book, Efthimis Efthimiathis, had maintained a leading role in teaching and learning within the domain of IR for a number of years. This book represents a posthumous example of his efforts in the area, as he passed away in April 2011. This book, his book, is dedicated to his memory.
Author: Allen B. Downey
Publisher: O'Reilly Germany
Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.
PROMISE Winter School 2013, Bressanone, Italy, February 4-8, 2013. Revised Tutorial Lectures
Author: Nicola Ferro
The research domains of information retrieval and databases have traditionally adopted different approaches to information management. However, in recent years, there has been an increasing cross-fertilization among the two fields and now many research challenges are transversal to them. With this in mind, a winter school was organized in Bressanone, Italy, in February 2013, within the context of the EU-funded research project PROMISE (Participative Research Laboratory for Multimedia and Multilingual Information Systems Evaluation). PROMISE aimed at advancing the experimental evaluation of complex multimedia and multilingual information systems in order to support individuals, commercial entities and communities, who design, develop, employ and improve such complex systems. The overall goal of PROMISE was to deliver a unified environment collecting data, knowledge, tools and methodologies and to help the user community involved in experimental evaluation. This book constitutes the outcome of the PROMISE Winter School 2013 and contains 9 invited lectures from the research domains of information retrieval and databases plus short papers of the best student poster awards. A large variety of topics are covered, including databases, information retrieval, experimental evaluation, metrics and statistics, semantic search, keyword search in databases, semi-structured search, evaluation both in information retrieval and databases, crowdsourcing and social media.
Algorithms and Heuristics
Author: David A. Grossman,Ophir Frieder
Publisher: Springer Science & Business Media
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.
Eine Einführung in die objektorientierte Modellierung
Author: Martina Seidl,Marion Brandsteidl,Christian Huemer,Gerti Kappel
Dieses Lehrbuch vermittelt die Grundlagen der objektorientierten Modellierung anhand von UML und bietet eine kompakte Einführung in die fünf Diagramme Klassendiagramm, Anwendungsfalldiagramm, Zustandsdiagramm, Sequenzdiagramm und Aktivitätsdiagramm. Diese decken die wesentlichen Konzepte ab, die für die durchgängige objektorientierte Modellierung in einem kompletten Softwareentwicklungsprozess benötigt werden. Besonderer Wert wird auf die Verdeutlichung des Zusammenspiels unterschiedlicher Diagramme gelegt. Die präsentierten Konzepte werden anhand von illustrativen Beispielen erklärt.
Third European Summer-School, ESSIR 2000 Varenna, Italy, September 11-15, 2000. Revised Lectures
Author: Italy) European Summer School in Information Retrieval 2000 (Varenna,Maristella Agosti,Fabio Crestani,Gabriella Pasi,European Summer School in Information Retrieval (3 : 2000 : Varenna)
Publisher: Springer Science & Business Media
Category: Business & Economics
Information Retrieval (IR) is concerned with the effective and efficient retrieval of information based on its semantic content. The central problem in IR is the quest to find the set of relevant documents, among a large collection containing the information sought, satisfying a user's information need usually expressed in a natural language query. Documents may be objects or items in any medium: text, image, audio, or indeed a mixture of all three. This book presents 12 revised lectures given at the Third European Summer School in Information Retrieval, ESSIR 2000, held at the Villa Monastero, Varenna, Italy, in September 2000. The first part of the book is devoted to the foundation of IR and related areas; the second part on advanced topics addresses various current issues, from usability aspects to Web searching and browsing.