Unsupervised Grammar Induction with Depth-bounded PCFG
Table 4: Performances of different systems for noun phrase recall and aggregated F1 scores on the Eve dataset.
['System', 'NP Recall', 'NP agg F1']
Surprisingly the D=2, K=15 model which has the lowest PARSEVAL scores is most accurate at discovering noun phrases. It has the highest scores on both evaluation metrics. The best model in terms of PARSEVAL scores, the D=1, K=15 DB-PCFG model, performs poorly among the DB-PCFG models, despite the fact that its NP Recall is higher than the competing systems. The low score of NP agg F1 of DB-PCFG at D1K15 shows a diffusion of induced syntactic categories when the model is trying to find a balance among labeling and branching decisions. The UPPARSE system, which is proposed as a base NP chunker, is relatively poor at NP recall by this definition.