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BK

54.72 / Künstliche Intelligenz

Classification

54.72 / Künstliche Intelligenz

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BK

54.72 / Künstliche Intelligenz

Classification

54.72 / Künstliche Intelligenz

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Abstract

Kostengünstige Massenspeicher und die zunehmende Vernetzung von Rechnern haben die Anzahl der Dokumente, auf die ein einzelnes Individuum zugreifen kann (bspw. Webseiten) oder die auf das Individuum einströmen (bspw. E-Mails), in den letzten Jahren rapide ansteigen lassen. In immer mehr Bereichen der Wirtschaft, Wissenschaft und Verwaltung nimmt der Bedarf an hochwertigen Information-Filtering und -Retrieval Werkzeugen zur Beherrschung der Informationsflut zu. Zur computergestützten Lösung dieser Problemstellung sind Modelle zur Repräsentation natürlichsprachlicher Dokumente erforderlich, um formale Kriterien für die automatisierte Auswahl relevanter Dokumente definieren zu können. Dominik Kuropka gibt in seiner Arbeit eine umfassende Übersicht über den Themenbereich der Suche und Filterung von natürlichsprachlichen Dokumenten. Es wird eine Vielzahl von Modellen aus Forschung und Praxis vorgestellt und evaluiert. Auf den Ergebnissen aufbauend wird das Potenzial von Ontologien in diesem Zusammenhang eruiert und es wird ein neues, ontologie-basiertes Modell für das Information-Filtering und -Retrieval erarbeitet, welches anhand von Text- und Code-Beispielen ausführlich erläutert wird. Das Buch richtet sich an Dozenten und Studenten der Informatik, Wirtschaftsinformatik und (Computer-)Linguistik sowie an Systemdesigner und Entwickler von dokumentenorientierten Anwendungssystemen und Werkzeugen.

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Abstract

Kostengünstige Massenspeicher und die zunehmende Vernetzung von Rechnern haben die Anzahl der Dokumente, auf die ein einzelnes Individuum zugreifen kann (bspw. Webseiten) oder die auf das Individuum einströmen (bspw. E-Mails), in den letzten Jahren rapide ansteigen lassen. In immer mehr Bereichen der Wirtschaft, Wissenschaft und Verwaltung nimmt der Bedarf an hochwertigen Information-Filtering und -Retrieval Werkzeugen zur Beherrschung der Informationsflut zu. Zur computergestützten Lösung dieser Problemstellung sind Modelle zur Repräsentation natürlichsprachlicher Dokumente erforderlich, um formale Kriterien für die automatisierte Auswahl relevanter Dokumente definieren zu können. Dominik Kuropka gibt in seiner Arbeit eine umfassende Übersicht über den Themenbereich der Suche und Filterung von natürlichsprachlichen Dokumenten. Es wird eine Vielzahl von Modellen aus Forschung und Praxis vorgestellt und evaluiert. Auf den Ergebnissen aufbauend wird das Potenzial von Ontologien in diesem Zusammenhang eruiert und es wird ein neues, ontologie-basiertes Modell für das Information-Filtering und -Retrieval erarbeitet, welches anhand von Text- und Code-Beispielen ausführlich erläutert wird. Das Buch richtet sich an Dozenten und Studenten der Informatik, Wirtschaftsinformatik und (Computer-)Linguistik sowie an Systemdesigner und Entwickler von dokumentenorientierten Anwendungssystemen und Werkzeugen.

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Abstract

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies. With the goal of accelerating research in this area, TREC created the first large test collections of full-text documents and standardized retrieval evaluation. The impact has been significant; since TREC's beginning in 1992, retrieval effectiveness has approximately doubled. TREC has built a variety of large test collections, including collections for such specialized retrieval tasks as cross-language retrieval and retrieval of speech. Moreover, TREC has accelerated the transfer of research ideas into commercial systems, as demonstrated in the number of retrieval techniques developed in TREC that are now used in Web search engines. This book provides a comprehensive review of TREC research, summarizing the variety of TREC results, documenting the best practices in experimental information retrieval, and suggesting areas for further research. The first part of the book describes TREC's history, test collections, and retrieval methodology. Next, the book provides "track" reports -- describing the evaluations of specific tasks, including routing and filtering, interactive retrieval, and retrieving noisy text. The final part of the book offers perspectives on TREC from such participants as Microsoft Research, University of Massachusetts, Cornell University, University of Waterloo, City University of New York, and IBM. The book will be of interest to researchers in information retrieval and related technologies, including natural language processing.

Footnote

Rez. in: JASIST 58(2007) no.6, S.910-911 (J.L. Vicedo u. J. Gomez): "The Text REtrieval Conference (TREC) is a yearly workshop hosted by the U.S. government's National Institute of Standards and Technology (NIST) that fosters and supports research in information retrieval as well as speeding the transfer of technology between research labs and industry. Since 1992, TREC has provided the infrastructure necessary for large-scale evaluations of different text retrieval methodologies. TREC impact has been very important and its success has been mainly supported by its continuous adaptation to the emerging information retrieval needs. Not in vain, TREC has built evaluation benchmarks for more than 20 different retrieval problems such as Web retrieval, speech retrieval, or question-answering. The large and intense trajectory of annual TREC conferences has resulted in an immense bulk of documents reflecting the different eval uation and research efforts developed. This situation makes it difficult sometimes to observe clearly how research in information retrieval (IR) has evolved over the course of TREC. TREC: Experiment and Evaluation in Information Retrieval succeeds in organizing and condensing all this research into a manageable volume that describes TREC history and summarizes the main lessons learned. The book is organized into three parts. The first part is devoted to the description of TREC's origin and history, the test collections, and the evaluation methodology developed. The second part describes a selection of the major evaluation exercises (tracks), and the third part contains contributions from research groups that had a large and remarkable participation in TREC. Finally, Karen Spark Jones, one of the main promoters of research in IR, closes the book with an epilogue that analyzes the impact of TREC on this research field.
... TREC: Experiment and Evaluation in Information Retrieval is a reliable and comprehensive review of the TREC program and has been adopted by NIST as the official history of TREC (see http://trec.nist.gov). We were favorably surprised by the book. Well structured and written, chapters are self-contained and the existence of references to specialized and more detailed publications is continuous, which makes it easier to expand into the different aspects analyzed in the text. This book succeeds in compiling TREC evolution from its inception in 1992 to 2003 in an adequate and manageable volume. Thanks to the impressive effort performed by the authors and their experience in the field, it can satiate the interests of a great variety of readers. While expert researchers in the IR field and IR-related industrial companies can use it as a reference manual, it seems especially useful for students and non-expert readers willing to approach this research area. Like NIST, we would recommend this reading to anyone who may be interested in textual information retrieval."

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Date

22. 3.2008 12:26:32

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Date

16.10.2013 19:22:44