Defense Date


Document Type


Degree Name

Master of Science


Computer Science

First Advisor

Bridget McInnes


One of the primary challenges for clinical Named Entity Recognition (NER) is the availability of annotated training data. Technical and legal hurdles prevent the creation and release of corpora related to electronic health records (EHRs). In this work, we look at the imapct of pseudo-data generation on clinical NER using gazetteering and thresholding utilizing a neural network model. We report that gazetteers can result in the inclusion of proper terms with the exclusion of determiners and pronouns in preceding and middle positions. Gazetteers that had higher numbers of terms inclusive to the original dataset had a higher impact. We also report that thresholding results in clear trend lines across the thresholds with some values oscillating around a fixed point at the most confidence points.


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