ezDI: A Supervised NLP System for Clinical Narrative Analysis

摘要

This paper describes the approach used by ezDI at the SemEval 2015 Task-14: "Analysis of Clinical Text". The task was divided into two embedded tasks. Task-1 required determining disorder boundaries (including the discontiguous ones) from a given set of clinical notes and normalizing the disorders by assigning a unique CUI from the UMLS/SNOMEDCT. Task-2 was about finding different type of modifiers for given disorder mention. Task-2 was divided further into two subtasks. In subtask-2a, gold set of disorder was already provided and system needed to just fill modifier types into the pre-specified slots. Subtask 2b did not provide any gold set of disorders and both the disorders and its related modifiers are to be identified by the system itself. In Task-1 our system was ranked first with F-score of 0.757 for strict evaluation and 0.788 for relaxed evaluation. In both Task-2a and 2b our system was placed second with weighted F-score of 0.88 and 0.795 respectively.