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Author: Robert M. Losee Publisher: Springer Nature ISBN: 303102317X Category : Computers Languages : en Pages : 59
Book Description
Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively. This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.
Author: Robert M. Losee Publisher: Springer Nature ISBN: 303102317X Category : Computers Languages : en Pages : 59
Book Description
Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively. This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.
Author: Alberto Apostolico Publisher: Springer Science & Business Media ISBN: 3540232109 Category : Computers Languages : en Pages : 345
Book Description
This book constitutes the refereed proceedings of the 11th International Conference on String Processing and Information Retrieval, SPIRE 2004, held in Padova, Italy, in October 2004. The 28 revised full papers and 16 revised short papers presented were carefully reviewed and selected from 123 submissions. The papers address current issues in string pattern searching and matching, string discovery, data compression, data mining, text mining, machine learning, information retrieval, digital libraries, and applications in various fields, such as bioinformatics, speech and natural language processing, Web links and communities, and multilingual data.
Author: David Carmel Publisher: Morgan & Claypool Publishers ISBN: 160845357X Category : Computers Languages : en Pages : 77
Book Description
Many information retrieval (IR) systems suffer from a radical variance in performance when responding to users' queries. Even for systems that succeed very well on average, the quality of results returned for some of the queries is poor. Thus, it is desirable that IR systems will be able to identify "difficult" queries so they can be handled properly. Understanding why some queries are inherently more difficult than others is essential for IR, and a good answer to this important question will help search engines to reduce the variance in performance, hence better servicing their customer needs. Estimating the query difficulty is an attempt to quantify the quality of search results retrieved for a query from a given collection of documents. This book discusses the reasons that cause search engines to fail for some of the queries, and then reviews recent approaches for estimating query difficulty in the IR field. It then describes a common methodology for evaluating the prediction quality of those estimators, and experiments with some of the predictors applied by various IR methods over several TREC benchmarks. Finally, it discusses potential applications that can utilize query difficulty estimators by handling each query individually and selectively, based upon its estimated difficulty. Table of Contents: Introduction - The Robustness Problem of Information Retrieval / Basic Concepts / Query Performance Prediction Methods / Pre-Retrieval Prediction Methods / Post-Retrieval Prediction Methods / Combining Predictors / A General Model for Query Difficulty / Applications of Query Difficulty Estimation / Summary and Conclusions
Author: Christopher D. Manning Publisher: Cambridge University Press ISBN: 1139472100 Category : Computers Languages : en Pages :
Book Description
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.
Author: Shlomo Geva Publisher: Springer ISBN: 3642037615 Category : Computers Languages : en Pages : 496
Book Description
I write with pleasurethis forewordto the proceedings of the 7th workshopof the Initiative for the Evaluation of XML Retrieval (INEX). The increased adoption of XML as the standard for representing a document structure has led to the development of retrieval systems that are aimed at e?ectively accessing XML documents. Providing e?ective access to large collections of XML documents is therefore a key issue for the success of these systems. INEX aims to provide the necessary methodological means and worldwide infrastructures for evaluating how good XML retrieval systems are. Since its launch in 2002, INEX has grown both in terms of number of p- ticipants and its coverage of the investigated retrieval tasks and scenarios. In 2002, INEX started with 49 registered participating organizations, whereas this number was more than 100 for 2008. In 2002, there was one main track, c- cerned with the ad hoc retrieval task, whereas in 2008, seven tracks in addition to the main ad hoc track were investigated, looking at various aspects of XML retrieval, from book search to entity ranking, including interaction aspects.
Author: Shaoping Ma Publisher: Springer ISBN: 3319480510 Category : Computers Languages : en Pages : 376
Book Description
This book constitutes the refereed proceedings of the 12th Information Retrieval Societies Conference, AIRS 2016, held in Beijing, China, in November/December 2016. The 21 full papers presented together with 11 short papers were carefully reviewed and selected from 57 submissions. The final programme of AIRS 2015 is divided in the following tracks: IR models and theories; machine learning and data mining for IR; IR applications and user modeling; personalization and recommendation; and IR evaluation.
Author: Management Association, Information Resources Publisher: IGI Global ISBN: 1466695633 Category : Computers Languages : en Pages : 2326
Book Description
Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Business Intelligence: Concepts, Methodologies, Tools, and Applications presents a comprehensive examination of business data analytics along with case studies and practical applications for businesses in a variety of fields and corporate arenas. Focusing on topics and issues such as critical success factors, technology adaptation, agile development approaches, fuzzy logic tools, and best practices in business process management, this multivolume reference is of particular use to business analysts, investors, corporate managers, and entrepreneurs in a variety of prominent industries.
Author: Max Kuhn Publisher: CRC Press ISBN: 1351609467 Category : Business & Economics Languages : en Pages : 266
Book Description
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Author: Joemon M. Jose Publisher: Springer Nature ISBN: 3030454428 Category : Computers Languages : en Pages : 709
Book Description
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.
Author: Robert M. Losee
Author: Robert M. Losee
Author: Alberto Apostolico
Author: David Carmel
Author: Christopher D. Manning
Author: Diane Kelly
Author: Shlomo Geva
Author: Shaoping Ma
Author: Management Association, Information Resources
Author: Max Kuhn
Author: Joemon M. Jose