We present a new methodology for producing low-dimensional word vectors based on distance geometry and dimensional reduction techniques. We use these word vectors in order to construct sentence vectors. We evaluate their usefulness in a sentence classification task performed by a simple artificial neural network. Compared to n-gram incidence vectors, the new methodology is shown to yield lower loss values.