The Smart Automatic Document Retrieval System An Illustration
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The SMART Retrieval System
Author | : Gerard Salton |
Publisher | : Englewood Cliffs, N.J : Prentice-Hall |
Total Pages | : 586 |
Release | : 1971 |
Genre | : Computers |
ISBN | : |
USA. Compilation of papers on technical aspects of fully automatic computer-based information retrieval systems, with particular reference to the experimental smart information system operated at harvard and cornell universities - covers theoretical developments (incl. System evaluation), language analysis techniques, the evaluation of document analysis methodology, user feedback procedures, etc. Diagrams, references and statistical tables.
The SMART retrieval system
Author | : Gerard Salton (ed) |
Publisher | : |
Total Pages | : 556 |
Release | : 1971 |
Genre | : Information storage and retrieval systems |
ISBN | : |
Document Retrieval Systems
Author | : Peter Willett |
Publisher | : London : Taylor Graham and the Institute of Information Scientists |
Total Pages | : 304 |
Release | : 1988 |
Genre | : Documentation |
ISBN | : |
Automatic Information Organization and Retrieval
Author | : Gerard Salton |
Publisher | : New York : McGraw-Hill |
Total Pages | : 540 |
Release | : 1968 |
Genre | : Computers |
ISBN | : |
Textbook on methodology of automation in documentation work - covers EDP, computerisation, dictionary construction and operations, storage of and research for information, mathematical analysis and statistical method, evaluation of methodology, etc. Bibliography pp. 485 to 498, and flow diagrams.
Text Mining
Author | : Sholom M. Weiss |
Publisher | : Springer Science & Business Media |
Total Pages | : 244 |
Release | : 2010-01-08 |
Genre | : Computers |
ISBN | : 0387345558 |
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.