Information Retrieval: Algorithms and HeuristicsSpringer Science & Business Media, 30.09.1998 - 254 Seiten Information Retrieval: Algorithms and Heuristics is a comprehensive introduction to the study of information retrieval covering both effectiveness and run-time performance. The focus of the presentation is on algorithms and heuristics used to find documents relevant to the user request and to find them fast. Through multiple examples, the most commonly used algorithms and heuristics needed are tackled. To facilitate understanding and applications, introductions to and discussions of computational linguistics, natural language processing, probability theory and library and computer science are provided. While this text focuses on algorithms and not on commercial product per se, the basic strategies used by many commercial products are described. Techniques that can be used to find information on the Web, as well as in other large information collections, are included. This volume is an invaluable resource for researchers, practitioners, and students working in information retrieval and databases. For instructors, a set of Powerpoint slides, including speaker notes, are available online from the authors. |
Inhalt
INTRODUCTION | 1 |
RETRIEVAL STRATEGIES | 11 |
RETRIEVAL UTILITIES | 83 |
EFFICIENCY ISSUES PERTAINING TO SEQUENTIAL IR SYSTEMS | 133 |
INTEGRATING STRUCTURED DATA AND TEXT | 153 |
PARALLEL INFORMATION RETRIEVAL SYSTEMS | 185 |
DISTRIBUTED INFORMATION RETRIEVAL | 201 |
THE TEXT RETRIEVAL CONFERENCE TREC | 221 |
FUTURE DIRECTIONS | 227 |
Andere Ausgaben - Alle anzeigen
Information Retrieval: Algorithms and Heuristics David A. Grossman,Ophir Frieder Eingeschränkte Leseprobe - 2012 |
Information Retrieval: Algorithms and Heuristics David A. Grossman,Ophir Frieder Keine Leseprobe verfügbar - 2012 |
Häufige Begriffe und Wortgruppen
ACM SIGIR Conference approach automatically clustering algorithms components compression computed concept D₁ DBMS described developed distributed information retrieval DocId document clustering document collection document frequency document length document retrieval document vector documents that contain entry estimate example function fuzzy set genetic algorithms identify implement improve indicates inference network information retrieval systems initial inverted index link matrix match measure n-grams neural network nodes non-relevant documents number of documents number of occurrences number of relevant obtained occur parallel partition percent posting list precision and recall probabilistic model probability processors query expansion query Q query terms relevance feedback relevance ranking relevant documents result set retrieval strategy run-time performance Salton SC(Q scanning Section semantic network silver truck similarity coefficient similarity matrix Sparck Jones stored t₁ term appears term frequency term weights Text Retrieval Text Retrieval Conference tf-idf thesaurus TREC truck tuples update user-defined operators vector space model
Verweise auf dieses Buch
Information Retrieval: A Health and Biomedical Perspective William Hersh Eingeschränkte Leseprobe - 2008 |
Power Laws in the Information Production Process: Lotkaian Informetrics Leo Egghe Keine Leseprobe verfügbar - 2005 |