Text Mining and its Applications

Author: Spiros Sirmakessis
Publisher: Springer
ISBN: 9783540452195
Release Date: 2012-12-06
Genre: Computers

The world of text mining is simultaneously a minefield and a gold mine. It is an exciting application field and an area of scientific research that is currently under rapid development. It uses techniques from well-established scientific fields (e.g. data mining, machine learning, information retrieval, natural language processing, case based reasoning, statistics and knowledge management) in an effort to help people gain insight, understand and interpret large quantities of (usually) semi-structured and unstructured data. Despite the advances made during the last few years, many issues remain umesolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identified, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the field of Text Mining -especially in relation to IT- and whether there still remain areas to be covered.

Text Mining und dessen Implementierung

Author: Norman Zänker
Publisher: diplom.de
ISBN: 9783842806283
Release Date: 2014-04-11
Genre: Computers

Inhaltsangabe:Einleitung: In der heutigen Zeit, in der der Umgang mit Informationsressourcen den Alltag bestimmt, ist es wichtig, dass es Systeme gibt, die gewährleisten, dass für den Nutzer relevante Informationen gesucht und auf die wichtigsten Fakten reduziert werden. Ein Großteil der gespeicherten Informationen, welche extrahiert werden sollen, sind dabei in Form von Textdokumenten vorhanden. Zu diesem Zweck gibt es in der Informatik ein Fachgebiet, das es sich zur Aufgabe gemacht hat, Analysewerkzeuge zur Bearbeitung von natürlich sprachigen Texten zu entwickeln. Diese Entwicklung hatte ihren Ursprung bereits in den Anfängen der Informatik und ist somit eines der ältesten Probleme der IT-Branche. Mit der erhöhten Zugänglichkeit der Informationen steigen die Anforderungen an Informationssysteme, von denen eine automatische Generierung und Aufbereitung von Wissen erwartet wird. Dabei wird die Entwicklung solcher Informationssysteme mit verschiedenen Problemen konfrontiert. Beispielsweise erschwert die schiere Masse an Daten die Auswahl der Informationsquellen. Allein das Volumen des Internets umfasst ca. 75 Mio. Webseiten, ganz zu schweigen von unternehmensinternen Datenbanken, Email-Verkehr und Dokumentenmanagementsysteme, deren Datenvolumen bereits im Jahr 2000 auf 1000 Petabyte geschätzt wurde. Da elektronische Medien in der modernen Zeit immer mehr an Bedeutung gewinnen, steigen auch die gespeicherten Informationen in unaufhaltsamem Maße fast exponentiell an. Dieser Trend wird auch als Information Overload bezeichnet. Erschwerend dabei ist, dass weder die Inhalte, noch der Zweck des Systems im World Wide Web klar definiert sind. Desweiteren macht die natürliche Sprache der einzelnen Informationsquellen zu schaffen. Solang die Daten strukturiert in einer Datenbank vorliegen, können sie von Informationssystemen ohne Probleme gelesen und die wichtigsten Informationen herausgefiltert werden. Dieses Verfahren ist bekannt unter dem Begriff Data-Mining . Bei natürlichen Texten liegt jedoch keine feste Datenstruktur vor, da Semantik und Syntax bei der Informationsgewinnung berücksichtigt werden müssen. Hinzu kommt noch, dass statistische Methoden eine große Rolle spielen, um die gewünschten Informationen aus den Texten zu gewinnen. Ohne entsprechende Systeme ist es somit unmöglich, effektiv mit den Informationen aus Texten umzugehen. Eine Technik, die es dennoch ermöglicht Textdatenbanken zu analysieren und Wissen aus unbekannten Texten zu [...]

Text Analysis Pipelines

Author: Henning Wachsmuth
Publisher: Springer
ISBN: 9783319257419
Release Date: 2015-12-02
Genre: Computers

This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics. Both web search and big data analytics aim to fulfill peoples’ needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.

Text Mining

Author: Ashok N. Srivastava
Publisher: CRC Press
ISBN: 1420059459
Release Date: 2009-06-15
Genre: Computers

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field Giving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search. The book begins with chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use. There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field.

Text Mining

Author: Michael W. Berry
Publisher: John Wiley & Sons
ISBN: 047068965X
Release Date: 2010-02-25
Genre: Mathematics

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.

Text Mining Wissensrohstoff Text

Author: Gerhard Heyer
Publisher:
ISBN: 3937137300
Release Date: 2006
Genre: Text Mining

Ein großer Teil des Weltwissens liegt in Form digitaler Texte im Internet und in Intranets. Diese digitalen Texte - die in den meisten natürlichen Sprachen vorliegen - stellen einen bedeutsamen und bisher kaum genutzten Wissensrohstoff dar. Lernen Sie in diesem ersten deutschen Lehrbuch zu diesem Thema, wie digitaler Text mit Hilfe des ”Text Mining“ für das Wissensmanagement aufbereitet, verarbeitet und genutzt werden kann. Die behandelten Themen in diesem Buch: Wissen und Text, Grundlagen der Bedeutungsanalyse, Textdatenbanken, Sprachstatistik, Clustering, Musteranalyse, Hybride Verfahren, Beispielanwendungen, Anhänge: Statistik und linguistische Grundlagen.

Practical Text Mining with Perl

Author: Roger Bilisoly
Publisher: John Wiley & Sons
ISBN: 9781118210505
Release Date: 2011-09-20
Genre: Computers

Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.perl.org). It covers mining ideas from several perspectives--statistics, data mining, linguistics, and information retrieval--and provides readers with the means to successfully complete text mining tasks on their own. The book begins with an introduction to regular expressions, a text pattern methodology, and quantitative text summaries, all of which are fundamental tools of analyzing text. Then, it builds upon this foundation to explore: Probability and texts, including the bag-of-words model Information retrieval techniques such as the TF-IDF similarity measure Concordance lines and corpus linguistics Multivariate techniques such as correlation, principal components analysis, and clustering Perl modules, German, and permutation tests Each chapter is devoted to a single key topic, and the author carefully and thoughtfully introduces mathematical concepts as they arise, allowing readers to learn as they go without having to refer to additional books. The inclusion of numerous exercises and worked-out examples further complements the book's student-friendly format. Practical Text Mining with Perl is ideal as a textbook for undergraduate and graduate courses in text mining and as a reference for a variety of professionals who are interested in extracting information from text documents.

Text Mining in den Sozialwissenschaften

Author: Matthias Lemke
Publisher: Springer-Verlag
ISBN: 9783658072247
Release Date: 2015-10-28
Genre: Social Science

Die Analyse von Sprache ermöglicht Rückschlüsse auf Gesellschaft und Politik. Im Zeitalter digitaler Massenmedien liegt Sprache als maschinenlesbarer Text in einer Menge vor, die ohne Hilfsmittel nicht mehr angemessen zu bewältigen ist. Die maschinelle Auswertung von Textdaten kann in den Sozialwissenschaften, die Text bislang in der Regel qualitativ und weniger quantitativ, also sprachstatistisch, analysieren, wertvolle neue Erkenntnisse liefern. Vor diesem Hintergrund führt der Band in die Verwendung von Text Mining in den Sozialwissenschaften ein. Anhand exemplarischer Analysen eines Korpus von 3,5 Millionen Zeitungsartikeln zeigt er für konkrete Forschungsfragen, wie Text Mining angewandt werden kann.

Text Mining

Author: Sholom M. Weiss
Publisher: Springer Science & Business Media
ISBN: 0387345558
Release Date: 2010-01-08
Genre: Computers

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.

Document Warehousing and Text Mining

Author: Dan Sullivan
Publisher: Wiley
ISBN: UOM:39015049985099
Release Date: 2001-03-07
Genre: Computers

What developers need to know about the rapidly growing technologies of document warehousing and text mining This unique book shows warehouse developers and managers how to build this new type of warehouse, how to organize free-form text for easy access, and, most importantly, how to exploit text mining techniques to provide timely and accurate information for decision-makers. The author covers the complete process of building and managing a document warehouse, including examples of actual implementations, a review of security issues and tools such as XML and Wide Area Information Servers and their selection criteria, and how text mining techniques are different from data mining techniques.

Text Mining

Author: Jürgen Franke
Publisher: Physica
ISBN: 3790800414
Release Date: 2003-03-18
Genre: Computers

Text Mining – Theoretical Aspects and Applications presents contributions from researchers from different disciplines. Each of them is studying the problem of mining text according to his scientific background: artificial intelligence, computational linguistics, document analysis, machine learning, information retrieval, pattern recognition. Their common goal is to analyse huge text collections in real world applications in order to support knowledge-intensive processes.

Text Mining Techniques for Healthcare Provider Quality Determination Methods for Rank Comparisons

Author: Cerrito, Patricia
Publisher: IGI Global
ISBN: 9781605667539
Release Date: 2009-08-31
Genre: Computers

The quest for quality in healthcare has led to attempts to develop models to determine which providers have the highest quality in healthcare, with the best outcomes for patients. Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons discusses the general practice of defining a patient severity index in order to make risk adjustments to compare patient outcomes across multiple providers with the intent of ranking the providers in terms of quality. This innovative reference source, valuable to medical practitioners, researchers, and academicians, brings together research from across the globe focusing on how severity indices are generally defined when determining the best outcome for patient

Text Mining

Author: Dominik Claussen
Publisher: GRIN Verlag
ISBN: 9783640193677
Release Date: 2008
Genre:

Studienarbeit aus dem Jahr 2008 im Fachbereich BWL - Sonstiges, Note: 2,3, Katholische Universitat Eichstatt-Ingolstadt (Wirtschaftswissenschaftliche Fakultat), 9 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Text Mining wird zur Suche und Ordnung von Dokumenten benotigt. Ausserdem kann Wissen aus den Texten gewonnen werden. Fur diese drei Ergebnisse des Text Mining bestehen zahlreiche Einsatzmoglichkeiten in Unternehmen. Da im Customer-Relationship-Management (CRM) viele Informationen uber Texte ausgetauscht werden, kann Text Mining dort gut verwendet werden. Um einen Einblick in das Thema zu bekommen, soll zuerst eine Einordnungen des Text Mining betrachtet werden. Grundlegend werden im ersten Teil auch einzelne Begriffe erlautert, ahnliche Verfahren abgrenzt, sowie eine Ubersicht fur sprachliche Problemfalle gegeben. Anschliessend wird der Prozess des Text Mining erlautert, die Erlauterung erfolgt entlang der Prozesskette. So wird erst die Textdatenbank, dann die maschinelle Sprachverarbeitung und abschliessend die Wissensgenerierung jeweils als Prozesselement vorgestellt. Um die Theorie abzurunden, soll ein Ausblick der Entwicklung des Text Mining, sowie ein praktisches Beispiel der Firma Media-Saturn gegeben werden.Zuletzt werden nochmal die Kernthesen zusammengefasst."

Survey of Text Mining II

Author: Michael W. Berry
Publisher: Springer Science & Business Media
ISBN: 1848000464
Release Date: 2007-12-10
Genre: Computers

This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. Authors highlight open research questions in document categorization, clustering, and trend detection. In addition, the book describes new application problems in areas such as email surveillance and anomaly detection.