# Target Word Masking for Location Metonymy Resolution

arXiv: https://arxiv.org/abs/2010.16097 · HTML: https://mlsanity.com/paper/2010.16097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

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

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [ConMeC: A Dataset for Metonymy Resolution with Common Nouns](https://mlsanity.com/paper/2502.06087/) (2025)
- [Impact of Target Word and Context on End-to-End Metonymy Detection](https://mlsanity.com/paper/2112.03256/) (2021)
- [Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges](https://mlsanity.com/paper/2406.16674/) (2024)

## Abstract (arXiv)

Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on BERT, without dependencies on taggers, parsers, curated dictionaries of place names, or other external resources. We show that our approach achieves the state-of-the-art on 5 datasets, surpassing conventional BERT models and benchmarks by a large margin. We also show that our approach generalises well to unseen data.