Integrating Information Retrieval with Artificial Neural Networks
Implementing a Modular Information Retrieval System using Artificial Neural Networks
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Information Retrieval (IR) is a field of computer science investigating the automated storage and retrieval of information, particularly documents. The amount of stored information has expanded rapidly and now, vast repositories of information on almost every conceivable subject are available to be searched. Effective IR involves: understanding the needs of users; handling the vagaries and ambiguities of language and human errors; and developing an efficient and accurate storage and search system. This book provides an analysis of IR techniques and systems assessing their strengths and weaknes...
Information Retrieval (IR) is a field of computer science investigating the automated storage and retrieval of information, particularly documents. The amount of stored information has expanded rapidly and now, vast repositories of information on almost every conceivable subject are available to be searched. Effective IR involves: understanding the needs of users; handling the vagaries and ambiguities of language and human errors; and developing an efficient and accurate storage and search system. This book provides an analysis of IR techniques and systems assessing their strengths and weaknesses. This analysis then provides the motivation for the proposed system of three integrated modules: a novel spell checker based on a binary neural network; a thesaurus generated from a dynamic growing neural network; and, an efficient word-to-document index. The book provides a detailed description of the implementation and evaluation of the proposed system. The IR analyses and system development should be useful to advanced undergraduate and postgraduate computer and library/information science students and researchers analysing and developing IR systems.
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