  We present our system for semantic frame induction that showed the best
performance in Subtask B.1 and finished as the runner-up in Subtask A of the
SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et
al., 2019). Our approach separates this task into two independent steps: verb
clustering using word and their context embeddings and role labeling by
combining these embeddings with syntactical features. A simple combination of
these steps shows very competitive results and can be extended to process other
datasets and languages.
