Download or read book Predicting Information Retrieval Performance written by Robert M. Losee and published by Springer Nature. This book was released on 2022-05-31 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Predicting Information Retrieval Performance written by Robert M. Losee and published by Springer. This book was released on 2018-12-19 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Retrieval Performance Prediction and Document Quality written by and published by . This book was released on 2007 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to predict retrieval performance has potential applications in many important IR (Information Retrieval) areas. In this thesis, we study the problem of predicting retrieval quality at the granularity of both the retrieved document set as a whole and individual retrieved documents. At the level of ranked lists of documents, we propose several novel prediction models that capture different aspects of the retrieval process that have a major impact on retrieval effectiveness. These techniques make performance prediction both effective and efficient in various retrieval settings including a Web search environment. As an application, we also provide a framework to address the problem of query expansion prediction. At the level of documents, we predict the quality of documents in the context of Web ad-hoc retrieval. We explore document features that are predictive of quality. Furthermore, we propose a document quality language model to improve retrieval effectiveness by incorporating quality information.

Download or read book Estimating the Query Difficulty for Information Retrieval written by David Carmel and published by Springer Nature. This book was released on 2022-05-31 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Predicting Information Retrieval Performance written by Robert M. Losee and published by Morgan & Claypool. This book was released on 2018-12-19 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Simulating Information Retrieval Test Collections written by David Hawking and published by Morgan & Claypool Publishers. This book was released on 2020-09-04 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulated test collections may find application in situations where real datasets cannot easily be accessed due to confidentiality concerns or practical inconvenience. They can potentially support Information Retrieval (IR) experimentation, tuning, validation, performance prediction, and hardware sizing. Naturally, the accuracy and usefulness of results obtained from a simulation depend upon the fidelity and generality of the models which underpin it. The fidelity of emulation of a real corpus is likely to be limited by the requirement that confidential information in the real corpus should not be able to be extracted from the emulated version. We present a range of methods exploring trade-offs between emulation fidelity and degree of preservation of privacy. We present three different simple types of text generator which work at a micro level: Markov models, neural net models, and substitution ciphers. We also describe macro level methods where we can engineer macro properties of a corpus, giving a range of models for each of the salient properties: document length distribution, word frequency distribution (for independent and non-independent cases), word length and textual representation, and corpus growth. We present results of emulating existing corpora and for scaling up corpora by two orders of magnitude. We show that simulated collections generated with relatively simple methods are suitable for some purposes and can be generated very quickly. Indeed it may sometimes be feasible to embed a simple lightweight corpus generator into an indexer for the purpose of efficiency studies. Naturally, a corpus of artificial text cannot support IR experimentation in the absence of a set of compatible queries. We discuss and experiment with published methods for query generation and query log emulation. We present a proof-of-the-pudding study in which we observe the predictive accuracy of efficiency and effectiveness results obtained on emulated versions of TREC corpora. The study includes three open-source retrieval systems and several TREC datasets. There is a trade-off between confidentiality and prediction accuracy and there are interesting interactions between retrieval systems and datasets. Our tentative conclusion is that there are emulation methods which achieve useful prediction accuracy while providing a level of confidentiality adequate for many applications.

Download or read book String Processing and Information Retrieval written by Alberto Apostolico and published by Springer Science & Business Media. This book was released on 2004-09-23 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Quality Issues in the Management of Web Information written by Gabriella Pasi and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research volume presents a sample of recent contributions related to the issue of quality-assessment for Web Based information in the context of information access, retrieval, and filtering systems. The advent of the Web and the uncontrolled process of documents' generation have raised the problem of declining quality assessment to information on the Web, by considering both the nature of documents (texts, images, video, sounds, and so on), the genre of documents ( news, geographic information, ontologies, medical records, products records, and so on), the reputation of information sources and sites, and, last but not least the actions performed on documents (content indexing, retrieval and ranking, collaborative filtering, and so on). The volume constitutes a compendium of both heterogeneous approaches and sample applications focusing specific aspects of the quality assessment for Web-based information for researchers, PhD students and practitioners carrying out their research activity in the field of Web information retrieval and filtering, Web information mining, information quality representation and management.

Download or read book Information Retrieval Technology written by Guido Zuccon and published by Springer. This book was released on 2016-01-21 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th Information Retrieval Societies Conference, AIRS 2015, held in Brisbane, QLD, Australia, in December 2015. The 29 full papers presented together with 11 short and demonstration papers, and the abstracts of 2 keynote lectures were carefully reviewed and selected from 92 submissions. The final programme of AIRS 2015 is divided in 10 tracks: Efficiency, Graphs, Knowledge Bases and Taxonomies, Recommendation, Twitter and Social Media, Web Search, Text Processing, Understanding and Categorization, Topics and Models, Clustering, Evaluation, and Social Media and Recommendation.

Download or read book Estimating the Query Difficulty for Information Retrieval written by David Carmel and published by Morgan & Claypool Publishers. This book was released on 2010 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Advances in Focused Retrieval written by Shlomo Geva and published by Springer. This book was released on 2009-09-01 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Information Retrieval Technology written by Shaoping Ma and published by Springer. This book was released on 2016-11-25 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Business Intelligence Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2015-12-29 with total page 2326 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Information Retrieval written by Joemon M. Jose and published by Springer Nature. This book was released on 2020-04-10 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Information Retrieval written by Mohand Boughanem and published by Springer Science & Business Media. This book was released on 2009-03-27 with total page 841 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 30th annual European Conference on Information Retrieval Research, ECIR 2009, held in Toulouse, France in April 2009. The 42 revised full papers and 18 revised short papers presented together with the abstracts of 3 invited lectures and 25 poster papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections on retrieval model, collaborative IR / filtering, learning, multimedia - metadata, expert search - advertising, evaluation, opinion detection, web IR, representation, clustering / categorization as well as distributed IR.

Download or read book Data Fusion in Information Retrieval written by Shengli Wu and published by Springer Science & Business Media. This book was released on 2012-04-05 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others: What are the key factors that affect the performance of data fusion algorithms significantly? What conditions are favorable to data fusion algorithms? CombSum and CombMNZ, which one is better? and why? What is the rationale of using the linear combination method? How can the best fusion option be found under any given circumstances?

Download or read book Advances in Information Retrieval written by Matthias Hagen and published by Springer Nature. This book was released on 2022-04-05 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 13185 and 13186 constitutes the refereed proceedings of the 44th European Conference on IR Research, ECIR 2022, held in April 2022, due to the COVID-19 pandemic. The 35 full papers presented together with 11 reproducibility papers, 13 CLEF lab descriptions papers, 12 doctoral consortium papers, 5 workshop abstracts, and 4 tutorials abstracts were carefully reviewed and selected from 395 submissions.