  This paper is dedicated to the design and evaluation of the first AMR parser
tailored for clinical notes. Our objective was to facilitate the precise
transformation of the clinical notes into structured AMR expressions, thereby
enhancing the interpretability and usability of clinical text data at scale.
Leveraging the colon cancer dataset from the Temporal Histories of Your Medical
Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing
continuous training. Our approach incorporates data augmentation techniques to
enhance the accuracy of AMR structure predictions. Notably, through this
learning strategy, our parser achieved an impressive F1 score of 88% on the
THYME corpus's colon cancer dataset. Moreover, our research delved into the
efficacy of data required for domain adaptation within the realm of clinical
notes, presenting domain adaptation data requirements for AMR parsing. This
exploration not only underscores the parser's robust performance but also
highlights its potential in facilitating a deeper understanding of clinical
narratives through structured semantic representations.
