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Augmenting Approximate Similarity Searching with Lexical Information
2005
Australasian Language Technology Association Workshop
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Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. However, the naïve nearest-neighbour approach to compare context vectors extracted from large corpora scales poorly. The Spatial Approximation Sample Hierarchy (SASH) is a data-structure for performing approximate nearest-neighbour queries, and has been previously used to improve the scalability of distributional similarity searches. We add lexical semantic
dblp:conf/acl-alta/GormanC05
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