UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations
Suwisa Kaewphan, Kai Hakala, Filip Ginter · 2014
In this paper we present our system par-ticipating in the SemEval-2014 Task 7 in both subtasks A and B, aiming at recognizing and normalizing disease and symptom mentions from electronic medi-cal records respectively. In subtask A, we used an existing NER system, NERsuite, with our own feature set tailored for this task. For subtask B, we combined word vector representations and supervised ma-chine learning to map the recognized men-tions to the corresponding UMLS con-cepts. Our system was placed 2nd and 5th out of 21 participants on subtasks A and B respectively showing competitive perfor-mance. 1