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Table 2 Performance of deep learning based dependency parsers on the progress notes (%)

From: Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison

Parser Corpus Word embeddings UAS LS LAS
Stanford parser Penn TreeBank Gigaword 75.76 84.23 71.21
ProgressNotes Gigaword 84.01 90.00 80.72
ProgressNotes MIMICIII 84.01 90.16 80.66
Bist-parser Penn TreeBank Gigaword 75.01 84.73 71.05
ProgressNotes Gigaword 82.26 89.69 78.94
ProgressNotes MIMICIII 81.78 89.31 78.42
Dependency-tf Penn TreeBank Gigaword 78.02   
ProgressNotes Gigaword 76.72   
ProgressNotes MIMICIII 77.09   
jPTDP-parser Penn TreeBank Gigaword 75.51 73.47 60.24
ProgressNotes Gigaword 77.59 83.60 71.58
ProgressNotes MIMICIII 79.35 85.10 73.61
  1. Highest performance in terms of each evaluation criterion is highlighted in boldface
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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