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Medical Segment Coloring of Clinical Notes
This paper proposes a deep learning-based method to identify the segments of a clinical note corresponding to ICD-9 broad categories which are further color-coded with respect to 17 ICD-9 categories.
Finite State Machine Pattern-Root Arabic Morphological Generator, Analyzer and Diacritizer
Accuracy results are: root computed from a word (92{\%}), word generation from a root (100{\%}), non-root properties of a word (97{\%}), and diacritization (84{\%}).
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