Information Retrieval Und Kunstliche Intelligenz
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| Author | : Helmut Jarosch |
| Publisher | : Springer-Verlag |
| Total Pages | : 262 |
| Release | : 2007-04-25 |
| Genre | : Business & Economics |
| ISBN | : 9783835005983 |
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.
| Author | : Jianfeng Gao |
| Publisher | : Springer Nature |
| Total Pages | : 217 |
| Release | : 2023-03-16 |
| Genre | : Computers |
| ISBN | : 3031230809 |
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.
| Author | : Fabio Crestani |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 414 |
| Release | : 2000-05-26 |
| Genre | : Business & Economics |
| ISBN | : 3790812994 |
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.
| Author | : Ralph Alberico |
| Publisher | : Westport : Meckler, cl990. |
| Total Pages | : 424 |
| Release | : 1990 |
| Genre | : Computers |
| ISBN | : |
| Author | : Tie-Yan Liu |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 282 |
| Release | : 2011-04-29 |
| Genre | : Computers |
| ISBN | : 3642142672 |
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.
| Author | : Fabrizio Sebastiani |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 640 |
| Release | : 2003-04-08 |
| Genre | : Computers |
| ISBN | : 3540012745 |
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.
| Author | : João Manuel R. S. Tavares |
| Publisher | : Springer Nature |
| Total Pages | : 630 |
| Release | : 2021-09-28 |
| Genre | : Technology & Engineering |
| ISBN | : 9811642842 |
This book gathers a collection of high-quality peer-reviewed research papers presented at International Conference on Cyber Intelligence and Information Retrieval (CIIR 2021), held at Institute of Engineering & Management, Kolkata, India during 20–21 May 2021. The book covers research papers in the field of privacy and security in the cloud, data loss prevention and recovery, high-performance networks, network security and cryptography, image and signal processing, artificial immune systems, information and network security, data science techniques and applications, data warehousing and data mining, data mining in dynamic environment, higher-order neural computing, rough set and fuzzy set theory, and nature-inspired computing techniques.
| Author | : Dharmender Saini |
| Publisher | : CRC Press |
| Total Pages | : 292 |
| Release | : 2021-12-14 |
| Genre | : Technology & Engineering |
| ISBN | : 1000484718 |
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.
| Author | : Gerald Jüttner |
| Publisher | : Walter de Gruyter |
| Total Pages | : 254 |
| Release | : 1988-01-01 |
| Genre | : Expert systems (Computer science) |
| ISBN | : 9783598107849 |
Informatik, Informationssystem, Dokumentationssystem, Thesaurus, EDV.
| Author | : Anand Vemula |
| Publisher | : Anand Vemula |
| Total Pages | : 30 |
| Release | : |
| Genre | : Computers |
| ISBN | : |
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
