Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. However, the naive nearest-neighbour approach to comparing context vectors extracted from large corpora scales poorly (O(n2) in the vocabulary size).In this paper, we compare several existing approaches to approximating the nearest-neighbour search for distributional similarity. We investigate the trade-off between efficiency and accuracy, and find that SASH (Houle and Sakuma, 2005) provides the best balance.
['James Gorman', 'James R. Curran']
Scaling Distributional Similarity to Large Corpora