# HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

## TL;DR

This paper introduces an unsupervised method for semantic frame induction that leverages contextualized word embeddings, achieving top performance in SemEval-2019 tasks by combining verb clustering and role labeling.

## Contribution

The authors propose a novel two-step approach using contextualized embeddings for verb clustering and role labeling, demonstrating competitive results in semantic frame induction.

## Key findings

- Achieved best performance in Subtask B.1 of SemEval-2019
- Finished as runner-up in Subtask A
- Simple combination of steps yields strong results

## Abstract

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.