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This paper presents our system submitted for 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(More)
OBJECTIVES In the Multiple Myeloma clinical registry at Heidelberg University Hospital, most data are extracted from discharge letters. Our aim was to analyze if it is possible to make the manual documentation process more efficient by using methods of natural language processing for multiclass classification of free-text diagnostic reports to automatically(More)
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