Abstract

Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. However, the naïve nearestneighbour approach to comparing context vectors extracted from large corpora scales poorly (O(n 2) in the vocabulary size). In this paper, we compare several existing approaches to approximating the nearestneighbour 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.

Citations