Search (3 results, page 1 of 1)

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Abstract

Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information on retrieval design and implementation questions is provided. This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms. The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described. This edition is a major expansion of the one published in 1998. Neuaufl. 2005: Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.

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Content

Teil 1: Grundlagen des Information Retrieval: Schwerpunkte des Information Retrieval mit Relevanz für die praktische Recherchedurchführung: Arbeitsschritte einer Recherche, Voraussetzungen für Online-Recherchen, Überblick über Arten von Datenbanken und über Hosts, Benutzerhilfen, Softwaretools, Retrievalsprachen und Kosten; Teil 2: Methoden des Information Retrieval: Einführung in die Methoden des Information Retrieval anhand ausgewählter Beispiele zu Retrievalsprachen, windows-basierten Retrievaltools und Web-Search-Möglichkeiten mittels hostspezifischer Suchoberflächen

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Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.