# Unsupervised Semantic Frame Induction using Triclustering

arXiv: https://arxiv.org/abs/1805.04715 · HTML: https://mlsanity.com/paper/1805.04715/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2018.

## Built on by (most cited first, 4 of 4)

- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Harvesting Event Schemas from Large Language Models](https://mlsanity.com/paper/2305.07280/) (2023)
- [Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction](https://mlsanity.com/paper/2105.13465/) (2021)
- [Adverbs, Surprisingly](https://mlsanity.com/paper/2305.19650/) (2023)

## Abstract (arXiv)

We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.