# Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction

arXiv: https://arxiv.org/abs/2105.13465 · HTML: https://mlsanity.com/paper/2105.13465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Fine-tune BERT for Extractive Summarization](https://mlsanity.com/paper/1903.10318/) (2019)
- [Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations](https://mlsanity.com/paper/1910.00194/) (2019)
- [Word Sense Induction with Neural biLM and Symmetric Patterns](https://mlsanity.com/paper/1808.08518/) (2018)
- [HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings](https://mlsanity.com/paper/1905.01739/) (2019)
- [Unsupervised Semantic Frame Induction using Triclustering](https://mlsanity.com/paper/1805.04715/) (2018)

## Built on by (most cited first, 1 of 1)

- [Semantic Frame Induction with Deep Metric Learning](https://mlsanity.com/paper/2304.14286/) (2023)

## Abstract (arXiv)

Contextualized word representations have proven useful for various natural language processing tasks. However, it remains unclear to what extent these representations can cover hand-coded semantic information such as semantic frames, which specify the semantic role of the arguments associated with a predicate. In this paper, we focus on verbs that evoke different frames depending on the context, and we investigate how well contextualized word representations can recognize the difference of frames that the same verb evokes. We also explore which types of representation are suitable for semantic frame induction. In our experiments, we compare seven different contextualized word representations for two English frame-semantic resources, FrameNet and PropBank. We demonstrate that several contextualized word representations, especially BERT and its variants, are considerably informative for semantic frame induction. Furthermore, we examine the extent to which the contextualized representation of a verb can estimate the number of frames that the verb can evoke.