Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Table 5: Number of instances (# sample) and predicate forms (# pred-) in the verbal (V) and nominal (N) domains of the processed CoNLL data.
['[EMPTY]', 'train # pred-', 'train # sample', 'dev # pred-', 'dev # sample', 'test # pred-', 'test # sample']
We apply verbalization to nominal predicates of the English CoNLL-2009 (CoNLL) dataset (hajivc2009conll), mapping nouns to their verb forms according to the morph-verbalization list . Then we select a set of shared verb predicates between the two domains and retain only instances having these shared predicates. Moreover, we keep only the following 15 frequent role labels in the two domains (a cut-off of 3): A0, A1, A2, A3, A4, A5, AM-ADV, AM-CAU, AM-DIR, AM-EXT, AM-LOC, AM-MNR, AM-NEG, AM-PRD, AM-TMP. In training, we use only the predicates attested with at least 20 instances in the nominal domain and at least 10 instances in the verbal domains, This rules out infrequent predicates which either trivially lead to a high score or lack labeled data to learn from.