The paper “HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings” by Saba Anwar, Dmitry Ustalov, Nikolay Arefyev, Chris Biemann, Simone Paolo Ponzetto, and Alexander Panchenko has been accepted for publication at SemEval 2019.
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.
HHMM is an abbreviation for Hansestadt Hamburg, Mannheim, and Moscow. It is chosen to avoid confusion with hidden Markov models.