Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Table 4: Clustering results on the CoNLL nominal training data.
['Models', 'PU', 'CO', 'F1']
We can see that SyntFun surpasses AllA0 by 2.28 in F1 measure, suggesting that syntactic functions remain good predictors of semantic roles in the nominal domain. However, Factorization does not work well when applied to the nominal data. Our model performs best, ourperforming the strongest baseline, Most-frequent, by 4.27% F1.