Capturing Syntagmatic Relations via Constituency Parsing | On the whole, the Berkeley parser processes IV words slightly better than our tagger, but processes OOV words significantly worse. |
Capturing Syntagmatic Relations via Constituency Parsing | The numbers in this table clearly shows the main weakness of the Berkeley parser is the the predictive power of the OOV words. |
Combining Both | We still use a Bagging model to integrate the discriminative tagger and the Berkeley parser . |
Combining Both | 11 indicate that the parsing accuracy of the Berkeley parser can be simply improved by inputting the Berkeley parser with the POS Bagging results. |
Introduction | We then present a comparative study of our tagger and the Berkeley parser , and show that the combination of the two models can significantly improve tagging accuracy. |
Experiments | We used the human-annotated parses for the sentences in the Penn Treebank, but parsed the Gigaword and BLLIP sentences with the Berkeley Parser . |
Experiments | PCFG-LA The Berkeley Parser in language model mode. |
Experiments | 7We use signatures generated by the Berkeley Parser . |