The Probabilistic Relevance Framework
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The Probabilistic Relevance Framework
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Author : Stephen Robertson
language : en
Publisher: Now Publishers Inc
Release Date : 2009
The Probabilistic Relevance Framework written by Stephen Robertson and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970-80s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account structure and link-graph information. Again, this has led to one of the most successful web-search and corporate-search algorithms, BM25F. The Probabilistic Relevance Framework: BM25 and Beyond presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25, BM25F. Besides presenting a full derivation of the PRF ranking algorithms, it provides many insights about document retrieval in general, and points to many open challenges in this area. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimization for models with free parameters. The Probabilistic Relevance Framework: BM25 and Beyond is self-contained and accessible to anyone with basic knowledge of probability and inference
Information Retrieval Models
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Author : Thomas Roelleke
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-07-01
Information Retrieval Models written by Thomas Roelleke and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-01 with Computers categories.
Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index
Information Retrieval Models
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Author : Thomas Roelleke
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Information Retrieval Models written by Thomas Roelleke 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.
Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index
A Generative Theory Of Relevance
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Author : Victor Lavrenko
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-14
A Generative Theory Of Relevance written by Victor Lavrenko and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-14 with Computers categories.
A modern information retrieval system must have the capability to find, organize and present very different manifestations of information – such as text, pictures, videos or database records – any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.
Mastering Elasticsearch 5 X
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Author : Bharvi Dixit
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-02-21
Mastering Elasticsearch 5 X written by Bharvi Dixit and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-21 with Computers categories.
Master the intricacies of Elasticsearch 5 and use it to create flexible and scalable search solutions About This Book Master the searching, indexing, and aggregation features in ElasticSearch Improve users' search experience with Elasticsearch's functionalities and develop your own Elasticsearch plugins A comprehensive, step-by-step guide to master the intricacies of ElasticSearch with ease Who This Book Is For If you have some prior working experience with Elasticsearch and want to take your knowledge to the next level, this book will be the perfect resource for you.If you are a developer who wants to implement scalable search solutions with Elasticsearch, this book will also help you. Some basic knowledge of the query DSL and data indexing is required to make the best use of this book. What You Will Learn Understand Apache Lucene and Elasticsearch 5's design and architecture Use and configure the new and improved default text scoring mechanism in Apache Lucene 6 Know how to overcome the pitfalls while handling relational data in Elasticsearch Learn about choosing the right queries according to the use cases and master the scripting module including new default scripting language, painlessly Explore the right way of scaling production clusters to improve the performance of Elasticsearch Master the searching, indexing, and aggregation features in Elasticsearch Develop your own Elasticsearch plugins to extend the functionalities of Elasticsearch In Detail Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. Elasticsearch leverages the capabilities of Apache Lucene, and provides a new level of control over how you can index and search even huge sets of data. This book will give you a brief recap of the basics and also introduce you to the new features of Elasticsearch 5. We will guide you through the intermediate and advanced functionalities of Elasticsearch, such as querying, indexing, searching, and modifying data. We'll also explore advanced concepts, including aggregation, index control, sharding, replication, and clustering. We'll show you the modules of monitoring and administration available in Elasticsearch, and will also cover backup and recovery. You will get an understanding of how you can scale your Elasticsearch cluster to contextualize it and improve its performance. We'll also show you how you can create your own analysis plugin in Elasticsearch. By the end of the book, you will have all the knowledge necessary to master Elasticsearch and put it to efficient use. Style and approach This comprehensive guide covers intermediate and advanced concepts in Elasticsearch as well as their implementation. An easy-to-follow approach means you'll be able to master even advanced querying, searching, and administration tasks with ease.
Mining Intelligence And Knowledge Exploration
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Author : Rajendra Prasath
language : en
Publisher: Springer
Release Date : 2014-12-01
Mining Intelligence And Knowledge Exploration written by Rajendra Prasath and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-01 with Computers categories.
This book constitutes the proceedings of the Second International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014, held in Cork, Ireland, in December 2014. The 40 papers presented were carefully reviewed and selected from 69 submissions. The papers cover topics such as information retrieval, feature selection, classification, clustering, image processing, network security, speech processing, machine learning, recommender systems, natural language processing, language, cognition and computation, and business intelligence.
Information Retrieval Technology
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Author : Mohamed Vall Mohamed Salem
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-12-02
Information Retrieval Technology written by Mohamed Vall Mohamed Salem and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-02 with Computers categories.
This book constitutes the refereed proceedings of the 7th Asia Information Retrieval Societies Conference AIRS 2011, held in Dubai, United Arab Emirates, in December 2011. The 31 revised full papers and 25 revised poster papers presented were carefully reviewed and selected from 132 submissions. All current aspects of information retrieval - in theory and practice - are addressed; the papers are organized in topical sections on information retrieval models and theories; information retrieval applications and multimedia information retrieval; user study, information retrieval evaluation and interactive information retrieval; Web information retrieval, scalability and adversarial information retrieval; machine learning for information retrieval; natural language processing for information retrieval; arabic script text processing and retrieval.
Current Challenges In Patent Information Retrieval
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Author : Mihai Lupu
language : en
Publisher: Springer
Release Date : 2017-03-24
Current Challenges In Patent Information Retrieval written by Mihai Lupu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-24 with Computers categories.
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.
Text Mining With Matlab
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Author : Rafael E. Banchs
language : en
Publisher: Springer Nature
Release Date : 2021-10-21
Text Mining With Matlab written by Rafael E. Banchs and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-21 with Computers categories.
Text Mining with MATLAB® provides a comprehensive introduction to text mining using MATLAB. It is designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The book is structured in three main parts: The first part, Fundamentals, introduces basic procedures and methods for manipulating and operating with text within the MATLAB programming environment. The second part of the book, Mathematical Models, is devoted to motivating, introducing, and explaining the two main paradigms of mathematical models most commonly used for representing text data: the statistical and the geometrical approach. Eventually, the third part of the book, Techniques and Applications, addresses general problems in text mining and natural language processing applications such as document categorization, document search, content analysis, summarization, question answering, and conversational systems. This second edition includes updates in line with the recently released “Text Analytics Toolbox” within the MATLAB product and introduces three new chapters and six new sections in existing ones. All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.
Process Oriented Semantic Web Search
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Author : D.T. Tran
language : en
Publisher: IOS Press
Release Date : 2011-02-22
Process Oriented Semantic Web Search written by D.T. Tran and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-22 with Computers categories.
The book is composed of two main parts. The first part is a general study of Semantic Web Search. The second part specifically focuses on the use of semantics throughout the search process, compiling a big picture of Process-oriented Semantic Web Search from different pieces of work that target specific aspects of the process. In particular, this book provides a rigorous account of the concepts and technologies proposed for searching resources and semantic data on the Semantic Web. To collate the various approaches and to better understand what the notion of Semantic Web Search entails, this book presents a general Semantic Web Search model. With respect to this model, the book provides a comprehensive discussion of the state-of-the-art. It elaborates on approaches for crawling, managing and searching Semantic Web resources as well as the various schemes proposed for ranking search results. Besides these specific approaches, search is also studied in a general multi-data-source scenario. This shall demonstrate how this work on search is extended and applied to the Web setting. A major feature of the book is that it considers search and the use of semantics for search also from a process point of view. Extending the general model, the book introduces the notion of Process-oriented Semantic Web Search, where semantics is exploited throughout the entire search process – from query construction to query processing up to result presentation and query refinement. Specific pieces of work targeting these individual steps of the process are combined to form a coherent and consistent picture of Process-oriented Semantic Web Search. In order to convey this general notion as well as the specific concepts and technologies developed for supporting the search process, this book presents a compilation of work called SemSearchPro and provides detailed descriptions on the underlying approaches.