Paper: Distributional Similarity Models: Clustering Vs. Nearest Neighbors

ACL ID P99-1005
Title Distributional Similarity Models: Clustering Vs. Nearest Neighbors
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1999
Authors

Distributional similarity is a useful notion in es- timating the probabilities of rare joint events. It has been employed both to cluster events ac- cording to their distributions, and to directly compute averages of estimates for distributional neighbors of a target event. Here, we examine the tradeoffs between model size and prediction accuracy for cluster-based and nearest neigh- bors distributional models of unseen events.