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J Am Med Inform Assoc 17:540-544 doi:10.1136/jamia.2010.004119
  • Research paper

Improving textual medication extraction using combined conditional random fields and rule-based systems

Table 3

Entry level precision, recall and F1-micro results achieved on 251gt with rule based and CRF-based solutions

Method Training set Inexact Exact
P R F1 P R F1
Rule based (challenge submission) – 0.8580 0.7623 0.8073 0.8408 0.7580 0.7972
CRF (whitespace, identity) 17gt 0.8951 0.5606 0.6894 0.8550 0.5461 0.6665
CRF (whitespace, standard) 17gt 0.9522 0.5418 0.6906 0.925 0.5539 0.6929
CRF (whitespace, domain) 17gt 0.8999 0.6765 0.7724 0.8732 0.6802 0.7647
CRF (custom, identity) 17gt 0.8794 0.5699 0.6916 0.8496 0.5607 0.6755
CRF (custom, standard) 17gt 0.9443 0.5493 0.6946 0.9203 0.5628 0.6985
CRF (custom, domain) 17gt 0.8822 0.6832 0.7701 0.8619 0.6753 0.7573
CRF (whitespace, domain) 1249rb 0.8616 0.7594 0.8073 0.8339 0.7567 0.7934
CRF (custom, domain) 1249rb 0.9137 0.7359 0.8152 0.8868 0.749 0.8121

  • CRF, conditional random fields.

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