An Introduction To Neural Information Retrieval
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An Introduction To Neural Information Retrieval
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Author : Bhaskar Mitra
language : en
Publisher: Foundations and Trends (R) in Information Retrieval
Release Date : 2018-12-23
An Introduction To Neural Information Retrieval written by Bhaskar Mitra and has been published by Foundations and Trends (R) in Information Retrieval this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-23 with categories.
Efficient Query Processing for Scalable Web Search will be a valuable reference for researchers and developers working on This tutorial provides an accessible, yet comprehensive, overview of the state-of-the-art of Neural Information Retrieval.
Introduction To Information Retrieval
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Author : Christopher D. Manning
language : en
Publisher: Cambridge University Press
Release Date : 2008-07-07
Introduction To Information Retrieval written by Christopher D. Manning and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-07 with Computers categories.
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.
Neural Approaches To Conversational Ai Question Answering Task Oriented Dialogues And Social Chatbots
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Author : Jianfeng Gao
language : en
Publisher: Foundations and Trends(r) in I
Release Date : 2019-02-21
Neural Approaches To Conversational Ai Question Answering Task Oriented Dialogues And Social Chatbots written by Jianfeng Gao and has been published by Foundations and Trends(r) in I this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
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.
Neural Approaches To Conversational Information Retrieval
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Author : Jianfeng Gao
language : en
Publisher: Springer Nature
Release Date : 2023-03-16
Neural Approaches To Conversational Information Retrieval written by Jianfeng Gao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-16 with Computers categories.
This book surveys recent advances in Conversational Information Retrieval (CIR), focusing on neural approaches that have been developed in the last few years. Progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken and typed interaction, increasing the need for more human-centric interactions in IR. The book contains nine chapters. Chapter 1 motivates the research of CIR by reviewing the studies on how people search and subsequently defines a CIR system and a reference architecture which is described in detail in the rest of the book. Chapter 2 provides a detailed discussion of techniques for evaluating a CIR system – a goal-oriented conversational AI system with a human in the loop. Then Chapters 3 to 7 describe the algorithms and methods for developing the main CIR modules (or sub-systems). In Chapter 3, conversational document search is discussed, which can be viewed as a sub-system of the CIR system. Chapter 4 is about algorithms and methods for query-focused multi-document summarization. Chapter 5 describes various neural models for conversational machine comprehension, which generate a direct answer to a user query based on retrieved query-relevant documents, while Chapter 6 details neural approaches to conversational question answering over knowledge bases, which is fundamental to the knowledge base search module of a CIR system. Chapter 7 elaborates various techniques and models that aim to equip a CIR system with the capability of proactively leading a human-machine conversation. Chapter 8 reviews a variety of commercial systems for CIR and related tasks. It first presents an overview of research platforms and toolkits which enable scientists and practitioners to build conversational experiences, and continues with historical highlights and recent trends in a range of application areas. Chapter 9 eventually concludes the book with a brief discussion of research trends and areas for future work. The primary target audience of the book are the IR and NLP research communities. However, audiences with another background, such as machine learning or human-computer interaction, will also find it an accessible introduction to CIR.
Introduction To Modern Information Retrieval
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Author : Gobinda G. Chowdhury
language : en
Publisher: Facet Publishing
Release Date : 2004
Introduction To Modern Information Retrieval written by Gobinda G. Chowdhury and has been published by Facet Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Business & Economics categories.
Blends together traditional and electronic-age views of information retrieval, covering the whole spectrum of storage and retrieval. A fully revised and updated edition of successful text covering many new areas including multimedia IR, user interfaces and digital libraries.
Issues In The Use Of Neural Networks In Information Retrieval
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Author : Iuliana F. Iatan
language : en
Publisher: Springer
Release Date : 2016-09-28
Issues In The Use Of Neural Networks In Information Retrieval written by Iuliana F. Iatan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-28 with Technology & Engineering categories.
This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality.It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules.Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.
Learning To Rank For Information Retrieval And Natural Language Processing Second Edition
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Author : Hang Li
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Learning To Rank For Information Retrieval And Natural Language Processing Second Edition written by Hang Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
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
Advances In Information Retrieval
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Author : Djoerd Hiemstra
language : en
Publisher: Springer Nature
Release Date : 2021-03-26
Advances In Information Retrieval written by Djoerd Hiemstra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Computers categories.
This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic. The 50 full papers presented together with 11 reproducibility papers, 39 short papers, 15 demonstration papers, 12 CLEF lab descriptions papers, 5 doctoral consortium papers, 5 workshop abstracts, and 8 tutorials abstracts were carefully reviewed and selected from 436 submissions. The accepted contributions cover the state of the art in IR: deep learning-based information retrieval techniques, use of entities and knowledge graphs, recommender systems, retrieval methods, information extraction, question answering, topic and prediction models, multimedia retrieval, and much more.
Advances In Information Retrieval
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Author : Joemon M. Jose
language : en
Publisher: Springer Nature
Release Date : 2020-04-10
Advances In Information Retrieval written by Joemon M. Jose and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-10 with Computers categories.
This two-volume set LNCS 12035 and 12036 constitutes the refereed proceedings of the 42nd European Conference on IR Research, ECIR 2020, held in Lisbon, Portugal, in April 2020.* The 55 full papers presented together with 8 reproducibility papers, 46 short papers, 10 demonstration papers, 12 invited CLEF papers, 7 doctoral consortium papers, 4 workshop papers, and 3 tutorials were carefully reviewed and selected from 457 submissions. They were organized in topical sections named: Part I: deep learning I; entities; evaluation; recommendation; information extraction; deep learning II; retrieval; multimedia; deep learning III; queries; IR – general; question answering, prediction, and bias; and deep learning IV. Part II: reproducibility papers; short papers; demonstration papers; CLEF organizers lab track; doctoral consortium papers; workshops; and tutorials. *Due to the COVID-19 pandemic, this conference was held virtually.
Advances In Information Retrieval
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Author : Nazli Goharian
language : en
Publisher: Springer Nature
Release Date :
Advances In Information Retrieval written by Nazli Goharian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.