TeamHCMUS: Analysis of Clinical Text

Abstract

We developed a system to participate in shared tasks on the analyzing clinical text. Our system approaches are both machine learning-based and rule-based. We applied the machine learning-based approach for Task 1: disorder identification, and the rule-based approach for Task 2: template slot filling for the disorder. In Task 1, we developed a supervised conditional random fields model that was based on a rich set of features, and used for predicting disorder mentions. In Task 2, we based on the dependency tree to build a rule set. This rule set was extracted from the training data and applied to fill values of disorder attribute types on the test data. The evaluation on the test data showed that our system achieved the F-score of 0.656 (0.685 in case of relaxed score) for Task 1 and the F*WA of 0.576 for Task 2A and the F*WA of 0.671 for Task 2B.