Learning Similarity Functions in Information Retrieval

Mandl, Thomas Learning Similarity Functions in Information Retrieval., 1998 . In EUFIT ‘98. 6th European Congress on Intelligent Techniques and Soft Computing. ,, Aachen, Germany, 8.-10.September 1998. [Conference paper]

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English abstract

Most models for Information Retrieval (IR) using neural networks are simple spreading activation models. Some of them were successfully applied to real world document collections. Nevertheless, they do not exploit the subsymbolic paradigma of neural processing. In this paper a model using a simple backpropagation network for IR is proposed. The COSIMIR model implements the central process in IR. It is a backpropagation network which calculates the similarity between a document and a query representation. The similarity function is learned through examples. Hence, it implements a cognitive similarity function. The first evaluation demonstrates that COSIMIR works well for short vectors.

Item type: Conference paper
Keywords: neural networks, information retrieval
Subjects: I. Information treatment for information services > IB. Content analysis (A and I, class.)
L. Information technology and library technology > LM. Automatic text retrieval.
L. Information technology and library technology > LS. Search engines.
Depositing user: Thomas Mandl
Date deposited: 29 Aug 2006
Last modified: 02 Oct 2014 12:04
URI: http://hdl.handle.net/10760/8042

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