Download or read book Probabilistic Topic Models for Information Retrieval and Concept Modeling written by Chaitanya Chandra Chemudugunta and published by . This book was released on 2009 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this dissertation, we investigate how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks and illustrate a variation of the model for metadata enhancement of digital libraries with multiple corpora. We next investigate the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we propose the concept-topic model which represents a document as a distribution over data-driven topics and semantic concepts. We extend this model to the hierarchical concept-topic model to incorporate concept hierarchies into the modeling framework. For all these models, we develop learning algorithms and demonstrate their utility with experiments conducted on real-world data sets.
Download or read book Probabilistic Topic Models written by Di Jiang and published by Springer Nature. This book was released on 2023-06-08 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the theoretical foundation and application of topic models. It provides readers with efficient means to learn about the technical principles underlying topic models. More concretely, it covers topics such as fundamental concepts, topic model structures, approximate inference algorithms, and a range of methods used to create high-quality topic models. In addition, this book illustrates the applications of topic models applied in real-world scenarios. Readers will be instructed on the means to select and apply suitable models for specific real-world tasks, providing this book with greater use for the industry. Finally, the book presents a catalog of the most important topic models from the literature over the past decades, which can be referenced and indexed by researchers and engineers in related fields. We hope this book can bridge the gap between academic research and industrial application and help topic models play an increasingly effective role in both academia and industry. This book offers a valuable reference guide for senior undergraduate students, graduate students, and researchers, covering the latest advances in topic models, and for industrial practitioners, sharing state-of-the-art solutions for topic-related applications. The book can also serve as a reference for job seekers preparing for interviews.
Download or read book Applications of Topic Models written by Jordan Boyd-Graber and published by Now Publishers. This book was released on 2017-07-13 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models.
Download or read book Text Mining with Probabilistic Topic Models written by Chaitanya Chemudugunta and published by LAP Lambert Academic Publishing. This book was released on 2010-09 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts.
Download or read book Uncertainty in Artificial Intelligence written by Laveen N. Kanal and published by North Holland. This book was released on 1986 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Download or read book Text Mining with R written by Julia Silge and published by "O'Reilly Media, Inc.". This book was released on 2017-06-12 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.
Download or read book Handbook of Latent Semantic Analysis written by Thomas K. Landauer and published by Psychology Press. This book was released on 2007-02-15 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Latent Semantic Analysis is the authoritative reference for the theory behind Latent Semantic Analysis (LSA), a burgeoning mathematical method used to analyze how words make meaning, with the desired outcome to program machines to understand human commands via natural language rather than strict programming protocols. The first book
Download or read book Extending Topic Models With Syntax and Semantics Relationships written by Elnaz Delpisheh and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Information Retrieval written by Christopher D. Manning and published by Cambridge University Press. This book was released on 2008-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Handbook of Mixed Membership Models and Their Applications written by Edoardo M. Airoldi and published by CRC Press. This book was released on 2014-11-06 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating more than 20 years of the editors' and contributors' statistical work in mixed membership modeling, this handbook shows how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, readers will discover how to characterize complex multivariate data in a range of areas.
Download or read book Statistical Language Models for Information Retrieval written by ChengXiang Zhai and published by Morgan & Claypool Publishers. This book was released on 2009 with total page 142 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 Subspace Latent Structure and Feature Selection written by Craig Saunders and published by Springer Science & Business Media. This book was released on 2006-05-16 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many of the papers in this proceedings volume were presented at the PASCAL Workshop entitled Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimization Perspectives which took place in Bohinj, Slovenia during February, 23–25 2005.
Download or read book Language Modeling for Information Retrieval written by W. Bruce Croft and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: A statisticallanguage model, or more simply a language model, is a prob abilistic mechanism for generating text. Such adefinition is general enough to include an endless variety of schemes. However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative techniques to classify text into predefined cat egories. The first statisticallanguage modeler was Claude Shannon. In exploring the application of his newly founded theory of information to human language, Shannon considered language as a statistical source, and measured how weH simple n-gram models predicted or, equivalently, compressed natural text. To do this, he estimated the entropy of English through experiments with human subjects, and also estimated the cross-entropy of the n-gram models on natural 1 text. The ability of language models to be quantitatively evaluated in tbis way is one of their important virtues. Of course, estimating the true entropy of language is an elusive goal, aiming at many moving targets, since language is so varied and evolves so quickly. Yet fifty years after Shannon's study, language models remain, by all measures, far from the Shannon entropy liInit in terms of their predictive power. However, tbis has not kept them from being useful for a variety of text processing tasks, and moreover can be viewed as encouragement that there is still great room for improvement in statisticallanguage modeling.
Download or read book The Semantic Web ISWC 2008 written by Amit P. Sheth and published by Springer Science & Business Media. This book was released on 2008-10-14 with total page 956 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Semantic Web Conference, ISWC 2008, held in Karlsruhe, Germany, during October 26-30, 2008. The volume contains 43 revised full research papers selected from a total of 261 submissions, of which an additional 3 papers were referred to the semantic Web in-use track; 11 papers out of 26 submissions to the semantic Web in-use track, and 7 papers and 12 posters accepted out of 39 submissions to the doctorial consortium. The topics covered in the research track are ontology engineering; data management; software and service engineering; non-standard reasoning with ontologies; semantic retrieval; OWL; ontology alignment; description logics; user interfaces; Web data and knowledge; semantic Web services; semantic social networks; and rules and relatedness. The semantic Web in-use track covers knowledge management; business applications; applications from home to space; and services and infrastructure.
Download or read book Advances in Information Retrieval written by Pavel Serdyukov and published by Springer. This book was released on 2013-03-12 with total page 919 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 35th European Conference on IR Research, ECIR 2013, held in Moscow, Russia, in March 2013. The 55 full papers, 38 poster papers and 10 demonstrations presented in this volume were carefully reviewed and selected from 287 submissions. The papers are organized in the following topical sections: user aspects; multimedia and cross-media IR; data mining; IR theory and formal models; IR system architectures; classification; Web; event detection; temporal IR, and microblog search. Also included are 4 tutorial and 2 workshop presentations.
Download or read book Practical Text Analytics written by Murugan Anandarajan and published by Springer. This book was released on 2018-10-19 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
Download or read book Mining Text Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2012-02-03 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
