| Mapping into VerbNet Thematic Roles | PropBank to VerbNet (hand) 79.17 :|:0.9 81.77 72.50 VerbNet (SemEval setting) 78.61 :|:0.9 81.28 71.84 PropBank to VerbNet (MF) 77.15 :|:0.9 79.09 71.90 VerbNet (CoNLL setting) 76.99 :|:0.9 79.44 70.88 Test on Brown PropB ank to VerbNet (MF) 64.79 :|:1.0 68.93 55.94 VerbNet ( CoNLL setting) 62.87 :|:1.0 67.07 54.69 |
| On the Generalization of Role Sets | Being aware that, in a real scenario, the sense information will not be available, we devised the second setting ( ‘CoNLL’ ), where the hand-annotated verb sense information was discarded. |
| On the Generalization of Role Sets | This is the setting used in the CoNLL 2005 shared task (Carreras and Marquez, 2005). |
| On the Generalization of Role Sets | In the second setting ( ‘CoNLL setting’ row in the same table) the PropBank classifier degrades slightly, but the difference is not statistically significant. |
| Evaluation Methodology | CoNLL The dependency tree format used in the 2006 and 2007 CoNLL shared tasks on dependency parsing. |
| Evaluation Methodology | KSDEP 1% CONLL RERANK NO—RERANK BERKELEY STANFORD ENJU ENJU—GENIA |
| Evaluation Methodology | Although the concept looks similar to CoNLL , this representa- |
| Experiments | CoNLL PTB HD SD PAS |
| Experiments | Dependency-based representations are competitive, while CoNLL seems superior to HD and SD in spite of the imperfect conversion from PTB to CoNLL . |
| Experiments | This might be a reason for the high performances of the dependency parsers that directly compute CoNLL dependencies. |
| Syntactic Parsers and Their Representations | The concept is therefore similar to CoNLL dependencies, though PAS expresses deeper relations, and may include reentrant structures. |
| Experiments | We evaluated our system using the setup of the Conll 2005 semantic role labeling task.2 Thus, we trained on Sections 2-21 of PropBank and used Section 24 as development data. |
| Experiments | We used the Char-niak parses provided by the Conll distribution. |
| Experiments | Our Transforms model takes as input the Char-niak parses supplied by the Conll release, and labels every node with Core arguments (ARGO—ARG5). |
| Introduction | Applying our combined simplificatiorVSRL model to the Conll 2005 task, we show a significant improvement over a strong baseline model. |
| Introduction | Our model outperforms all but the best few Conll 2005 systems, each of which uses multiple different automatically-generated parses (which would likely improve our model). |