Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction
Table 2: PARSEVAL scores for different constituency grammar induction systems.
['System', 'WSJ20test Rec', 'WSJ20test Prec', 'WSJ20test F1', 'CTB20 Rec', 'CTB20 Prec', 'CTB20 F1', 'NEGRA20 Rec', 'NEGRA20 Prec', 'NEGRA20 F1']
The model described in this paper shows strong performance in all languages. On English and Chinese, this model achieves the new state-of-the-art recall and F1 numbers. On German, this model also achieves the best recall scores among all models, showing that more constituents found in the gold annotation are discovered. It is worth noting that the CCL and UPPARSE models do take advantage of additional linguistic constraints, e.g. using punctuation as delimiters of constituents. Experiments described in this paper show that this system can perform better than or competitive with these existing models without similar heuristics and constraints.