Computing Word Classes Using Spectral Clustering
Table 1: Results for the SRL experiments.
['[BOLD] Feature Set', '[BOLD] precision', '[BOLD] recall', '[ITALIC] F1']
This is a very interesting result, considering that Brown clustering is tailored for word clustering by incorporating a statistical language model in the target function used for the clustering itself, as opposed to spectral clustering which takes no lexical considerations into account during the clustering process. Both models improve over the baseline (K-means) by approximately 0.04.