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) 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.


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.

See also