Computing Word Classes Using Spectral Clustering
Table 2: Results for the dependency parsing experiments.
['[BOLD] Feature Set', '[BOLD] UAS']
For each experiment we report unlabeled attachment score (UAS). The oracle result for SRL reveals the complementary nature of the two methods, achieving a significant increase of 0.075-0.078 in F1 score over using each model separately (an improvement of 12.5%) , outperforming even the best model which is based on syntactic features by 0.047 in F1 score This interesting result means that the information captured by the spectral method may be quite different from the one captured by the Brown clusters.