| Experiment Results & Error Analyses | Table 3 shows the agreement between the HP SG backbone and CoNLL dependency in unlabeled attachment score ( UAS ). |
| Experiment Results & Error Analyses | UAS are reported on all complete test sets, as well as fully parsed subsets (suffixed with “-p”>. |
| Experiment Results & Error Analyses | Most notable is that the dependency backbone achieved over 80% UAS on BROWN, which is close to the performance of state-of-the-art statistical dependency parsing systems trained on WSJ (see Table 5 and Table 4). |
| Evaluation Results | The quality of the parser is measured by the parsing accuracy or the unlabeled attachment score ( UAS ), i.e., the percentage of tokens with correct head. |
| Evaluation Results | Two types of scores are reported for comparison: “UAS without p” is the UAS score without all punctuation tokens and “UAS with p” is the one with all punctuation tokens. |
| Evaluation Results | Table 5 shows the results achieved by other researchers and ours ( UAS with p), which indicates that our parser outperforms any other ones 4. |