RALI: Automatic Weighting of Text Window Distances

Systems using text windows to model word contexts have mostly been using fixed-sized windows and uniform weights. The window size is often selected by trial and error to maximize task results. We propose a non-supervised method for selecting weights for each window distance, effectively removing the need to limit window sizes, by maximizing the mutual generation of two sets of samples of the same word. Experiments on Semeval Word Sense Disambiguation tasks showed considerable improvements.