Bøger i The Information Retrieval Series serien

This open access book provides an introduction and an overview of learning to quantify (a.k.a. ¿quantification¿), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (¿biased¿) class proportion estimates.The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research.The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (¿macrö) data rather than on individual (¿micrö) data.

Information visualization offers a way to reveal hidden patterns in a visual presentation and allows users to seek information from a visual perspective. Readers of this book will gain an in-depth understanding of the current state of information retrieval visualization.

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.

Explores the concepts and techniques of Web mining. This book presents the Web mining material from an information search perspective, focusing on issues relating to the efficiency, feasibility, scalability and usability of searching techniques for Web mining. It is designed for researchers and developers of Web information systems.

Document Computing: Technologies for Managing Electronic Document Collections discusses the important aspects of document computing and recommends technologies and techniques for document management, with an emphasis on the processes that are appropriate when computers are used to create, access, and publish documents.

With information retrieval a growing field of research, teaching it requires new resources. This book aims to provide theoretical and practical ideas for teaching IR, a topic which has up to now suffered from a lack of literature on its pedagogical aspects.

Document Computing: Technologies for Managing Electronic Document Collections discusses the important aspects of document computing and recommends technologies and techniques for document management, with an emphasis on the processes that are appropriate when computers are used to create, access, and publish documents.

This book covers content recognition in text, elaborating on past and current most successful algorithms and their application in a variety of settings: news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text.

With information retrieval a growing field of research, teaching it requires new resources. This book aims to provide theoretical and practical ideas for teaching IR, a topic which has up to now suffered from a lack of literature on its pedagogical aspects.

This book presents concepts underlying Collaborative Information Seeking, discusses CIS as a standalone domain, describes applications of CIS and examines its implications in such fields as computer-supported cooperative work and human-computer interaction.

A statisticallanguage model, or more simply a language model, is a prob abilistic mechanism for generating text.

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.

TThe Turn analyzes the research of information seeking and retrieval (IS&R) and proposes a new direction of integrating research in these two areas: the fields should turn off their separate and narrow paths and construct a new avenue of research.

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.

The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion.

New Directions in Cognitive Information Retrieval presents an exciting new direction for research into cognitive oriented information retrieval (IR) research, a direction based on an analysis of the user's problem situation and cognitive behavior when using the IR system.

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.

Searches often return either large numbers of matches or no suitable matches at all. The type of search system that we propose in this book can suggest ways of refining or relaxing the query to assist a user in the search process.

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.

Karen Sparck Jones is one of the major figures of 20th century and early 21st Century computing and information processing.

Searches often return either large numbers of matches or no suitable matches at all. The type of search system that we propose in this book can suggest ways of refining or relaxing the query to assist a user in the search process.

aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation.

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, the author presents feature-based retrieval models.

The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion.

This book presents a new way to look at topical relevance in information retrieval and offers a new method for modeling exchangeable sequences of discrete random variables which does not make any assumptions about the data and can also handle rare events.

TThe Turn analyzes the research of information seeking and retrieval (IS&R) and proposes a new direction of integrating research in these two areas: the fields should turn off their separate and narrow paths and construct a new avenue of research.

New Directions in Cognitive Information Retrieval presents an exciting new direction for research into cognitive oriented information retrieval (IR) research, a direction based on an analysis of the user's problem situation and cognitive behavior when using the IR system.

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.

aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation.

This book covers content recognition in text, elaborating on past and current most successful algorithms and their application in a variety of settings: news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text.