Capturing Nonlinear Structure in Word Spaces through Dimensionality Reduction

Dimensionality reduction has been shown to improve processing and information extraction from high dimensional data. Word space algorithms typically employ linear reduction techniques that assume the space is Euclidean. We investigate the effects of extracting nonlinear structure in the word space using Locality Preserving Projections, a reduction algorithm that performs manifold learning. We apply this reduction to two common word space models and show improved performance over the original models on benchmarks.