This paper presents our system submitted for SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiM-SUM;Schneider, Hovy, et al., 2016). We extend AMALGrAM (Schneider and Smith, 2015) by tapping two additional information sources. The first information source uses a semantic knowledge base (YAGO3; Suchanek et al., 2007) to improve supersense tagging (SST) for named entities. The second information source employs word embeddings (GloVe; Pennington et al., 2014) to capture fine-grained latent semantics and therefore improving the supersense identification for both nouns and verbs. We conduct a detailed evaluation and error analysis for our features and come to the conclusion that both our extensions lead to an improved detection for SST.

The low average error rates and high average F1-scores of each pipeline demonstrate the suitability of the investigated NPL methods. However, it was also shown that there is no best practice for an automatic classification of data elements from free-text diagnostic reports.