Learning Similarity Functions in Information Retrieval
نویسنده
چکیده
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.
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