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Table 6 The performance comparison of our method with baseline methods

From: A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

Language Method # exp. # correct Precision Recall F1
Chinese GUTime 512 429 0.838 0.241 0.375
HeidelTime 954 824 0.864 0.463 0.603
CMedTEX 2293 1645 0.717 0.925 0.808
TEER_C 1761 1613 0.916 0.907 0.912
TEER 1761 1657 0.941 0.932 0.936
English NLTK Timex 106 61 0.575 0.153 0.242
GUTime 101 88 0.871 0.221 0.353
IllinoisTExtractor 352 287 0.815 0.721 0.765
Heideltime 395 327 0.828 0.822 0.825
TEXer 360 340 0.944 0.854 0.897
TEER 368 349 0.948 0.877 0.911