MUC-7 (State of the art)
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- Performance measure: F = 2 * Precision * Recall / (Recall + Precision)
- Precision: percentage of named entities found by the algorithm that are correct
- Recall: percentage of named entities defined in the corpus that were found by the program
- Exact calculation of precision and recall is explained in the MUC scoring software
- Training data: Training section of MUC-7 dataset
- Dryrun data: Dryrun section of MUC-7 dataset
- Testing data: Formal section of MUC-7 dataset
Table of results
| System name | Short description | System type (1) | Main publications | Software | Results |
|---|---|---|---|---|---|
| Annotator | Human annotator | - | MUC-7 proceedings | - | 97.60% |
| LTG | Best MUC-7 participant | H | Mikheev, Grover and Moens (1998) | - | 93.39% |
| Balie | Unsupervised approach: no prior training | U | Nadeau, Turney and Matwin (2006) | sourceforge.net | 77.71% (2) |
| Baseline | Vocabulary transfer from training to testing | S | Whitelaw and Patrick (2003) | - | 58.89% (2) |
- (1) System type: R = hand-crafted rules, S = supervised learning, U = unsupervised learning, H = hybrid
- (2) Calculated on Enamex types only.
References
Mikheev, A., Grover, C. and Moens, M. (1998). Description of the LTG system used for MUC-7. Proceedings of the Seventh Message Understanding Conference (MUC-7). Fairfax, Virginia.
Nadeau, D., Turney, P. D. and Matwin, S. (2006) Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity. Proceedings 19th Canadian Conference on Artificial Intelligence. Québec, Canada.
Whitelaw, C. and Patrick, J. (2003) Evaluating Corpora for Named Entity Recognition Using Character-Level Features. Proceeding of the 16th Australian Conference on AI. Perth, Australia.