| Analysis | 9Default: we ran the Berkeley parser in its default ‘fast’ mode; the output kr-best lists are ordered by max-rule-score. |
| Analysis | Table 3: Parsing results for reranking 50-best lists of Berkeley parser (Dev is WSJ section 22 and Test is WSJ section 23, all lengths). |
| Introduction | For example, in the Berkeley parser (Petrov et al., 2006), about 20% of the errors are prepositional phrase attachment errors as in Figure l, where a preposition-headed (IN) phrase was assigned an incorrect parent in the implied dependency tree. |
| Introduction | Here, the Berkeley parser (solid blue edges) incorrectly attaches from debt to the noun phrase $ 30 billion whereas the correct attachment (dashed gold edges) is to the verb raising. |
| Introduction | Figure l: A PP attachment error in the parse output of the Berkeley parser (on Penn Treebank). |
| Parsing Experiments | We also evaluate the utility of web-scale features on top of a state-of—the-art constituent parser — the Berkeley parser (Petrov et al., 2006), an unlexical-ized phrase-structure parser. |
| Parsing Experiments | Our baseline system is the Berkeley parser , from which we obtain k-best lists for the development set (WSJ section 22) and test set (WSJ section 23) using a grammar trained on all the training data (WSJ sections 2-21).8 To get k-best lists for the training set, we use 3-fold jackknifing where we train a grammar |
| Parsing Experiments | Table 2: Oracle Fl-scores for kr-best lists output by Berkeley parser for English WSJ parsing (Dev is section 22 and Test is section 23, all lengths). |
| Abstract | We demonstrate that our method is faster than coarse-to-fine pruning, exemplified in both the Charniak and Berkeley parsers, by empirically comparing our parser to the Berkeley parser using the same grammar and under identical operating conditions. |
| Conclusion and Future Work | 2We run the Berkeley parser with the default search parameterization to achieve the fastest possible parsing time. |
| Conclusion and Future Work | Using this framework, we have shown that we can decrease parsing time by 65% over a standard beam-search without any loss in accuracy, and parse significantly faster than both the Berkeley parser and Chart Constraints. |
| Results | Both our parser and the Berkeley parser are written in Java, both are run with Viterbi decoding, and both parse with the same grammar, so a direct comparison of speed and accuracy is fair.2 |