Download or read book The Probabilistic Relevance Framework written by Stephen Robertson and published by Now Publishers Inc. This book was released on 2009 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Information Retrieval Models written by Thomas Roelleke and published by Morgan & Claypool Publishers. This book was released on 2013-07-01 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book A Generative Theory of Relevance written by Victor Lavrenko and published by Springer. This book was released on 2010-11-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Statistical Language Models for Information Retrieval written by Chengxiang Zhai and published by Springer Nature. This book was released on 2022-05-31 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details. Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions

Download or read book Information Retrieval Models written by Thomas Roelleke and published by Springer Nature. This book was released on 2022-05-31 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Information Retrieval written by Stefan Buttcher and published by MIT Press. This book was released on 2016-02-12 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to information retrieval, the foundation for modern search engines, that emphasizes implementation and experimentation. Information retrieval is the foundation for modern search engines. This textbook offers an introduction to the core topics underlying modern search technologies, including algorithms, data structures, indexing, retrieval, and evaluation. The emphasis is on implementation and experimentation; each chapter includes exercises and suggestions for student projects. Wumpus—a multiuser open-source information retrieval system developed by one of the authors and available online—provides model implementations and a basis for student work. The modular structure of the book allows instructors to use it in a variety of graduate-level courses, including courses taught from a database systems perspective, traditional information retrieval courses with a focus on IR theory, and courses covering the basics of Web retrieval. In addition to its classroom use, Information Retrieval will be a valuable reference for professionals in computer science, computer engineering, and software engineering.

Download or read book Introduction to Information Retrieval and Quantum Mechanics written by Massimo Melucci and published by Springer. This book was released on 2015-12-08 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Databases and Information Systems written by Tatjana Welzer and published by Springer Nature. This book was released on 2019-08-28 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019, held in Bled, Slovenia, in September 2019. The 27 full papers presented were carefully reviewed and selected from 103 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They focus on data mining and machine learning, data warehouses and big data technologies, semantic data processing, and data modeling. They are organized in the following topical sections: data mining; machine learning; document and text databases; big data; novel applications; ontologies and knowledge management; process mining and stream processing; data quality; optimization; theoretical foundation and new requirements; and data warehouses.

Download or read book Semantic Keyword Based Search on Structured Data Sources written by Andrea Calì and published by Springer. This book was released on 2017-02-13 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Second COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2016, held in Cluj-Napoca, Romania, in September 2016. The 15 revised full papers and 2 invited papers are reviewed and selected from 18 initial submissions and cover the areas of keyword extraction, natural language searches, graph databases, information retrieval techniques for keyword search and document retrieval.

Download or read book Current Challenges in Patent Information Retrieval written by Mihai Lupu and published by Springer. This book was released on 2017-03-24 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hybrid Artificial Intelligent Systems written by Pablo García Bringas and published by Springer Nature. This book was released on 2022-09-11 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022, held in Salamanca, Spain, in September 2022. The 43 full papers presented in this book were carefully reviewed and selected from 67 submissions. They were organized in topical sections as follows: bioinformatics; data mining and decision support systems; deep learning; evolutionary computation; HAIS applications; image and speech signal processing; and optimization techniques.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Peter A. Flach and published by Springer. This book was released on 2012-09-08 with total page 904 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Download or read book Information Retrieval Technology written by Yuexian Hou and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Information Retrieval Societies Conference, AIRS 2012, held in Tianjin, China, in December 2012. The 22 full papers and 26 poster presentations included in this volume were carefully reviewed and selected from 77 submissions. They are organized in topical sections named: IR models; evaluation and user studies; NLP for IR; machine learning and data mining; social media; IR applications; multimedia IT and indexing; collaborative and federated search; and the poster session.

Download or read book Advances in Information Retrieval written by Maarten de Rijke and published by Springer. This book was released on 2014-03-24 with total page 857 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 36th European Conference on IR Research, ECIR 2014, held in Amsterdam, The Netherlands, in April 2014. The 33 full papers, 50 poster papers and 15 demonstrations presented in this volume were carefully reviewed and selected from 288 submissions. The papers are organized in the following topical sections: evaluation, recommendation, optimization, semantics, aggregation, queries, mining social media, digital libraries, efficiency, and information retrieval theory. Also included are 3 tutorial and 4 workshop presentations.

Download or read book Advances in Information Retrieval written by Djoerd Hiemstra and published by Springer Nature. This book was released on 2021-03-29 with total page 760 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Peggy Cellier and published by Springer Nature. This book was released on 2020-03-27 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.

Download or read book Building Recommendation Systems in Python and JAX written by Bryan Bischof Ph.D and published by "O'Reilly Media, Inc.". This book was released on 2023-12-04 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases. You'll learn: The data essential for building a RecSys How to frame your data and business as a RecSys problem Ways to evaluate models appropriate for your system Methods to implement, train, test, and deploy the model you choose Metrics you need to track to ensure your system is working as planned How to improve your system as you learn more about your users, products, and business case