@inproceedings{gritta-etal-2017-vancouver,
title = "{V}ancouver Welcomes You! Minimalist Location Metonymy Resolution",
author = "Gritta, Milan and
Pilehvar, Mohammad Taher and
Limsopatham, Nut and
Collier, Nigel",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1115",
doi = "10.18653/v1/P17-1115",
pages = "1248--1259",
abstract = "Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.",
}
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%0 Conference Proceedings
%T Vancouver Welcomes You! Minimalist Location Metonymy Resolution
%A Gritta, Milan
%A Pilehvar, Mohammad Taher
%A Limsopatham, Nut
%A Collier, Nigel
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F gritta-etal-2017-vancouver
%X Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.
%R 10.18653/v1/P17-1115
%U https://aclanthology.org/P17-1115
%U https://doi.org/10.18653/v1/P17-1115
%P 1248-1259
Markdown (Informal)
[Vancouver Welcomes You! Minimalist Location Metonymy Resolution](https://aclanthology.org/P17-1115) (Gritta et al., ACL 2017)
ACL
- Milan Gritta, Mohammad Taher Pilehvar, Nut Limsopatham, and Nigel Collier. 2017. Vancouver Welcomes You! Minimalist Location Metonymy Resolution. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1248–1259, Vancouver, Canada. Association for Computational Linguistics.