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

arXiv: https://arxiv.org/abs/1905.01739 · HTML: https://mlsanity.com/paper/1905.01739/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 5 of 5)

- [Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering](https://mlsanity.com/paper/2105.13466/) (2021)
- [Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction](https://mlsanity.com/paper/2105.13465/) (2021)
- [Semantic Frame Induction with Deep Metric Learning](https://mlsanity.com/paper/2304.14286/) (2023)
- [Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction](https://mlsanity.com/paper/2305.13944/) (2023)
- [FrameEOL: Semantic Frame Induction using Causal Language Models](https://mlsanity.com/paper/2510.09097/) (2025)

## Abstract (arXiv)

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.