Learning Similarities for Text Documents using Neural Networks

Learning algorithms for neural networks follow either a supervised or a unsupervised scheme. In the first case a teacher provides a target for each pattern in the learning set, while in the second one no target is provided and the learning process aims at fitting the network to the data distribu- tion. In this paper we propose a learning algorithm which, somehow, lies in between these two schemes. The supervisor does not provide a specific target for each example but he specifies a set of relationships among pairs of input patterns. The neural network is trained to map the examples into points of the output space which meet the topological constraints related to the relationships provided by the supervisor. This algorithm is applied to realize an adaptive dimensionality reduction for the vector space representation of text documents. We present a set of experimental results showing that the algorithm is capable of exploiting the semantic relationships implicitly contained in the user’s feedback.