@inproceedings{li-etal-2020-target,
title = "Target Word Masking for Location Metonymy Resolution",
author = "Li, Haonan and
Vasardani, Maria and
Tomko, Martin and
Baldwin, Timothy",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.330/",
doi = "10.18653/v1/2020.coling-main.330",
pages = "3696--3707",
abstract = "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."
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%0 Conference Proceedings
%T Target Word Masking for Location Metonymy Resolution
%A Li, Haonan
%A Vasardani, Maria
%A Tomko, Martin
%A Baldwin, Timothy
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F li-etal-2020-target
%X 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.
%R 10.18653/v1/2020.coling-main.330
%U https://aclanthology.org/2020.coling-main.330/
%U https://doi.org/10.18653/v1/2020.coling-main.330
%P 3696-3707
Markdown (Informal)
[Target Word Masking for Location Metonymy Resolution](https://aclanthology.org/2020.coling-main.330/) (Li et al., COLING 2020)
ACL
- Haonan Li, Maria Vasardani, Martin Tomko, and Timothy Baldwin. 2020. Target Word Masking for Location Metonymy Resolution. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3696–3707, Barcelona, Spain (Online). International Committee on Computational Linguistics.