Recent work on the problem of detecting synonymy through corpus analysis has used the Test of English as a Foreign Language (TOEFL) as a benchmark. However, this test involves as few as 80 questions, prompting questions regarding the statistical significance of reported results. We overcome this limitation by generating a TOEFL-like test using WordNet, containing thousands of questions and composed only of words occurring with sufficient corpus frequency to support sound distributional comparisons. Experiments with this test lead us to a similarity measure which significantly outperforms the best proposed to date. Analysis suggests that a strength of this measure is its relative robustness against polysemy.

We present a fast yet highly effective stochastic algorithm, Simmered Greedy Optimization (SG(N)) for solving the coclustering problem: to simultaneously cluster two finite sets by maximizing the mutual information between the clusterings. (Clustering one set by this criterion is a special case.) This is a combinatorial optimization problem of great interest for deriving maximally predictive feature sets. Co-clustering has found applications in many areas, particularly statistical natural language processing and bioinformatics. We report results of tests on a suite of statistical natural language problems, comparing SG(N) with simulated annealing and a publicly available implementation of co-clustering. In all cases we obtain superior results with far less computation using SG(N).