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One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining – the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents. Developed from the authors’ highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material. Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.
Discover data-driven learning methods with the third volume of this extraordinary three-volume set.
This book constitutes the refereed proceedings of the First International Conference on Applied Algorithms, ICAA 2014, held in Kolkata, India, in January 2014. ICAA is a new conference series with a mission to provide a quality forum for researchers working in applied algorithms. Papers presenting original contributions related to the design, analysis, implementation and experimental evaluation of efficient algorithms and data structures for problems with relevant real-world applications were sought, ideally bridging the gap between academia and industry. The 21 revised full papers presented together with 7 short papers were carefully reviewed and selected from 122 submissions.
Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.
This book is based on first-hand personal experiences; nevertheless it is not about guilt or innocence. It is a handbook; guidance for those Americans, who may one day have to go to prison. This is a directory to the idiosyncrasies of the American ‘Justice Industry’ and to inmates, guards, lawyers, judges, prosecutors and incarceration facilities within it. By and large the workings of this self-perpetuating ‘industry’ are little known to the general public. Here is a detailed guide for all those unfortunate Americans who one day may fall into the hands of this relentless ‘industry’. It is a known fact that the United States has the highest number of incarcerated people in the world, hence mathematically speaking any American, including you the reader could easily be part of this sad statistics. By reading about events, persons and places described in this book, you the reader will be prepared (somewhat) to face this special section of society that (so far) had been locked away from you and the American public. ***** “There are two kinds of people my friends: The one who gets caught and the one yet to be caught..... every son of a bitch out there is guilty of something, including the judge and the jury who convicted me.” A quote from Orlando – a federal inmate serving a life sentence.
This book assumes very little or no knowledge of how computers work, and shows how to write understandable programs in Java. Even though most readers will not wish to become professional programmers, programming is fun and useful, and, in today's world it is important for professionals in any field to appreciate what computers can (and cannot) do well. To reach this level of understanding, Per Brinch Hansen goes beyond the routine skills of a computer user and explains the art of programming in some depth, allowing readers to write Java programs for use on the WWW or company's Intranet. Although a book about programming with Java, the same methods can be used for systematic programming in such languages as C, Fortran, and Pascal. The book makes a splendid text for a one semester course on beginning programming and is backed by teaching aids available at the author's Website.
This volume contains the proceedings of the 3rd International Conference on AdvancesinInformationSystems(ADVIS)heldinIzmir,Turkey,20–22October, 2004. This was the third conference dedicated to the memory of Prof. Esen Ozkarahan. We are very proud to continue this tradition and keep the memory of this outstanding scientist. The third conference covered many of the topics of the second one: databases and data warehouses, information systems development and management, - formation retrieval, distributed and parallel data processing, and evolutionary algorithms. Besides them some of the hot topics related to information systems were included in the scope of this conference, such as data mining and kno- edge discovery, Web information systems development, information privacy and security, multimedia information systems, and network management. This year we received 203 submissions from which the Program Committee selected 61 papers for presentation at the conference. The success of the conference was dependent upon the hard work of a large number of people. We gratefully acknowledge the contribution of the members of the Program Committee who did their best to review all submitted papers. We also thank all the specialists who helped us in reviewing the papers. We appreciated the constant support and help from the Rector of Dokuz Eylul University, Prof. Dr. Emin Alici. I would like to express my personal gratitude to Natalya Cheremnykh and Olga Drobyshevich for their help in producing the camera-ready version of these proceedings.
Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.
This book constitutes the refereed proceedings of the Second International Conference on Knowledge Science, Engineering and Management, KSEM 2007, held in Melbourne, Australia, in November 2007. The 42 revised full papers and 28 revised short papers presented together with five invited talks were carefully reviewed and selected. The papers provide new ideas and report research results in the broad areas of knowledge science, knowledge engineering, and knowledge management.