Advances in Information Retrieval

Advances in Information Retrieval PDF

Author: Fabrizio Sebastiani

Publisher: Springer Science & Business Media

Published: 2003-04-08

Total Pages: 640

ISBN-13: 3540012745

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This book constitutes the refereed proceedings of the 25th European Conference on Information Retrieval Research, ECIR 2003, held in Pisa, Italy, in April 2003. The 31 revised full papers and 16 short papers presented together with two invited papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on IR and the Web; retrieval of structured documents; collaborative filtering and text mining; text representation and natural language processing; formal models and language models for IR; machine learning and IR; text categorization; usability, interactivity, and visualization; and architectural issues and efficiency.

Information Retrieval und künstliche Intelligenz

Information Retrieval und künstliche Intelligenz PDF

Author: Helmut Jarosch

Publisher: Springer-Verlag

Published: 2007-04-25

Total Pages: 262

ISBN-13: 9783835005983

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Die effiziente Nutzung der Ressource „Wissen“ gilt als einer der kritischen Erfolgsfaktoren für Unternehmen, doch durch die Informationsüberflutung wird es immer schwieriger, vorhandenes Wissen aufzufinden. Die bislang verfügbaren Suchmaschinen und Information-Retrieval-Systeme bieten insbesondere ungeübten Benutzern wenig Unterstützung bei der Online-Recherche. Helmut Jarosch entwickelt einen Ansatz, mit dem Methoden der Künstlichen Intelligenz (KI) in die Kommunikation mit einem Information-Retrieval-System eingebunden werden. Dadurch kann ein Suchergebnis mit hoher Vollständigkeit und Genauigkeit erzielt werden. Dies wird am Beispiel eines „KI-Assistenten“ beschrieben, der den Benutzer bei der Formulierung seiner Anfrage unterstützt und so das Niveau der Benutzerkommunikation verbessert. Dabei werden einerseits Verfahren der Filterung und andererseits Methoden des nicht-überwachten und des überwachten Lernens angewendet.

Mastering the RAG: A Practical Guide to Deploying AI-Powered Data Retrieval and Generation in Your Enterprise -ERP, SAP, SFDC

Mastering the RAG: A Practical Guide to Deploying AI-Powered Data Retrieval and Generation in Your Enterprise -ERP, SAP, SFDC PDF

Author: Anand Vemula

Publisher: Anand Vemula

Published:

Total Pages: 30

ISBN-13:

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Mastering the RAG: Unleash the Power of AI in Your Enterprise Mastering the RAG: A Practical Guide to Deploying AI-Powered Data Retrieval and Generation in Your Enterprise (ERP, SAP, SFDC) equips you to harness the transformative power of Retrieval-Augmented Generation (RAG) for your enterprise applications. This book is your one-stop guide to implementing RAG with industry leaders like Oracle ERP, SAP, and Salesforce (SFDC), unlocking new levels of efficiency and data-driven insights. Imagine a world where AI streamlines your workflows, intelligently retrieves data from your core enterprise applications, and generates comprehensive reports or creative text formats at your command. That's the power of RAG. This practical guide takes you step-by-step through the entire deployment process, from selecting the right Large Language Model (LLM) to building a user-friendly interface. Part 1: Unveiling the RAG Potential Demystify the RAG pattern: Grasp the core concepts and how it revolutionizes data retrieval and generation within enterprise applications. Discover the advantages: Explore the tangible benefits of RAG for ERP, SAP, and SFDC users, including faster information retrieval, improved report generation, and enhanced automation. Identify use cases: Learn how RAG can be applied to real-world scenarios across various departments, from generating sales forecasts in SFDC to creating comprehensive financial reports in Oracle ERP. Part 2: Charting Your RAG Implementation Journey Prepare for deployment: Understand the necessary pre-requisites, including identifying compatible data sources within your enterprise applications and choosing the most suitable LLM for your specific needs. Dive deep into implementation: This section provides a detailed roadmap for setting up the retrieval component, integrating the LLM, and building a user-friendly interface or chatbot for seamless interaction. Security matters: Learn best practices for safeguarding sensitive enterprise data throughout the RAG deployment process. Part 3: Optimizing and Refining Your RAG Perfecting performance: Discover techniques for testing and evaluating your RAG system to ensure accuracy, mitigate bias, and promote explainability. User feedback and iteration: Learn how to incorporate user feedback into continuous improvement cycles to refine your RAG and maximize its effectiveness. Mastering the RAG empowers you to become a leader in adopting cutting-edge AI solutions within your enterprise. This book equips you with the knowledge and practical steps to unlock a new era of data-driven decision making and streamline workflows across Oracle ERP, SAP, and SFDC

Learning to Rank for Information Retrieval

Learning to Rank for Information Retrieval PDF

Author: Tie-Yan Liu

Publisher: Springer Science & Business Media

Published: 2011-04-29

Total Pages: 282

ISBN-13: 3642142672

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Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.

Computational Intelligence for Information Retrieval

Computational Intelligence for Information Retrieval PDF

Author: Dharmender Saini

Publisher: CRC Press

Published: 2021-12-14

Total Pages: 303

ISBN-13: 1000484726

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This book provides a thorough understanding of the integration of computational intelligence with information retrieval including content-based image retrieval using intelligent techniques, hybrid computational intelligence for pattern recognition, intelligent innovative systems, and protecting and analysing big data on cloud platforms. The book aims to investigate how computational intelligence frameworks are going to improve information retrieval systems. The emerging and promising state-of-the-art of human–computer interaction is the motivation behind this book. The book covers a wide range of topics, starting from the tools and languages of artificial intelligence to its philosophical implications, and thus provides a plethora of theoretical as well as experimental research, along with surveys and impact studies. Further, the book aims to showcase the basics of information retrieval and computational intelligence for beginners, as well as their integration, and challenge discussions for existing practitioners, including using hybrid application of augmented reality, computational intelligence techniques for recommendation systems in big data, and a fuzzy-based approach for characterization and identification of sentiments.

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 PDF

Author: Jianfeng Gao

Publisher: Foundations and Trends(r) in I

Published: 2019-02-21

Total Pages: 184

ISBN-13: 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.

Soft Computing in Information Retrieval

Soft Computing in Information Retrieval PDF

Author: Fabio Crestani

Publisher: Springer Science & Business Media

Published: 2000-05-26

Total Pages: 414

ISBN-13: 3790812994

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Information retrieval (IR) aims at defining systems able to provide a fast and effective content-based access to a large amount of stored information. The aim of an IR system is to estimate the relevance of documents to users' information needs, expressed by means of a query. This is a very difficult and complex task, since it is pervaded with imprecision and uncertainty. Most of the existing IR systems offer a very simple model of IR, which privileges efficiency at the expense of effectiveness. A promising direction to increase the effectiveness of IR is to model the concept of "partially intrinsic" in the IR process and to make the systems adaptive, i.e. able to "learn" the user's concept of relevance. To this aim, the application of soft computing techniques can be of help to obtain greater flexibility in IR systems.

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