Approximate searching for distributional similarity
James Gorman, James Curran · 2005
Distributional similarity requires large volumes of data to accurately represent infrequent words. However, the nearest-neighbour approach to finding synonyms suffers from poor scalability. The Spatial Approximation Sample Hierarchy (SASH), proposed by Houle (2003b), is a data structure for approximate nearest-neighbour queries that balances the efficiency/approximation trade-off. We have intergrated this into an existing distributional similarity system, tripling efficiency with a minor accuracy penalty.