Natural Language Information Retrieval

Natural Language Information Retrieval
Author: T. Strzalkowski
Publisher: Springer Science & Business Media
Total Pages: 407
Release: 2013-04-17
Genre: Language Arts & Disciplines
ISBN: 9401723885
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The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa tion access has exploded. Emerging applications in computer-assisted infor mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.

Graph-based Natural Language Processing and Information Retrieval

Graph-based Natural Language Processing and Information Retrieval
Author: Rada Mihalcea
Publisher: Cambridge University Press
Total Pages: 201
Release: 2011-04-11
Genre: Computers
ISBN: 1139498827
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Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Information Retrieval and Natural Language Processing

Information Retrieval and Natural Language Processing
Author: Sheetal S. Sonawane
Publisher: Springer Nature
Total Pages: 186
Release: 2022-02-22
Genre: Mathematics
ISBN: 981169995X
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This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.

Introduction to Information Retrieval

Introduction to Information Retrieval
Author: Christopher D. Manning
Publisher: Cambridge University Press
Total Pages:
Release: 2008-07-07
Genre: Computers
ISBN: 1139472100
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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.

Natural Language Processing and Information Retrieval

Natural Language Processing and Information Retrieval
Author: Tanveer Siddiqui
Publisher: Oxford University Press, USA
Total Pages: 426
Release: 2008-05
Genre: Computers
ISBN:
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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.

Information Retrieval

Information Retrieval
Author: Ayse Goker
Publisher: John Wiley & Sons
Total Pages: 320
Release: 2009-12-15
Genre: Technology & Engineering
ISBN: 9780470033630
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This book is an essential reference to cutting-edge issues and future directions in information retrieval Information retrieval (IR) can be defined as the process of representing, managing, searching, retrieving, and presenting information. Good IR involves understanding information needs and interests, developing an effective search technique, system, presentation, distribution and delivery. The increased use of the Web and wider availability of information in this environment led to the development of Web search engines. This change has brought fresh challenges to a wider variety of users’ needs, tasks, and types of information. Today, search engines are seen in enterprises, on laptops, in individual websites, in library catalogues, and elsewhere. Information Retrieval: Searching in the 21st Century focuses on core concepts, and current trends in the field. This book focuses on: Information Retrieval Models User-centred Evaluation of Information Retrieval Systems Multimedia Resource Discovery Image Users’ Needs and Searching Behaviour Web Information Retrieval Mobile Search Context and Information Retrieval Text Categorisation and Genre in Information Retrieval Semantic Search The Role of Natural Language Processing in Information Retrieval: Search for Meaning and Structure Cross-language Information Retrieval Performance Issues in Parallel Computing for Information Retrieval This book is an invaluable reference for graduate students on IR courses or courses in related disciplines (e.g. computer science, information science, human-computer interaction, and knowledge management), academic and industrial researchers, and industrial personnel tracking information search technology developments to understand the business implications. Intermediate-advanced level undergraduate students on IR or related courses will also find this text insightful. Chapters are supplemented with exercises to stimulate further thinking.

Natural Language Processing for Online Applications

Natural Language Processing for Online Applications
Author: Peter Jackson
Publisher: John Benjamins Publishing
Total Pages: 243
Release: 2007-06-05
Genre: Computers
ISBN: 9027292442
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This text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation.

Language Modeling for Information Retrieval

Language Modeling for Information Retrieval
Author: W. Bruce Croft
Publisher: Springer Science & Business Media
Total Pages: 253
Release: 2013-04-17
Genre: Computers
ISBN: 9401701717
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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.

Foundations of Statistical Natural Language Processing

Foundations of Statistical Natural Language Processing
Author: Christopher Manning
Publisher: MIT Press
Total Pages: 719
Release: 1999-05-28
Genre: Language Arts & Disciplines
ISBN: 0262303795
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Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Neural Approaches to Conversational AI: Question Answering, Task-Oriented Dialogues and Social Chatbots

Neural Approaches to Conversational AI: Question Answering, Task-Oriented Dialogues and Social Chatbots
Author: Jianfeng Gao
Publisher: Foundations and Trends(r) in I
Total Pages: 184
Release: 2019-02-21
Genre: Computers
ISBN: 9781680835526
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This monograph is the first survey of neural approaches to conversational AI that targets Natural Language Processing and Information Retrieval audiences. It provides a comprehensive survey of the neural approaches to conversational AI that have been developed in the last few years, covering QA, task-oriented and social bots with a unified view of optimal decision making.The authors draw connections between modern neural approaches and traditional approaches, allowing readers to better understand why and how the research has evolved and to shed light on how they can move forward. They also present state-of-the-art approaches to training dialogue agents using both supervised and reinforcement learning. Finally, the authors sketch out the landscape of conversational systems developed in the research community and released in industry, demonstrating via case studies the progress that has been made and the challenges that are still being faced.Neural Approaches to Conversational AI is a valuable resource for students, researchers, and software developers. It provides a unified view, as well as a detailed presentation of the important ideas and insights needed to understand and create modern dialogue agents that will be instrumental to making world knowledge and services accessible to millions of users in ways that seem natural and intuitive.