@inproceedings{stowe-etal-2019-linguistic,
title = "Linguistic Analysis Improves Neural Metaphor Detection",
author = "Stowe, Kevin and
Moeller, Sarah and
Michaelis, Laura and
Palmer, Martha",
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1034",
doi = "10.18653/v1/K19-1034",
pages = "362--371",
abstract = "In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.",
}
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%0 Conference Proceedings
%T Linguistic Analysis Improves Neural Metaphor Detection
%A Stowe, Kevin
%A Moeller, Sarah
%A Michaelis, Laura
%A Palmer, Martha
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F stowe-etal-2019-linguistic
%X In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.
%R 10.18653/v1/K19-1034
%U https://aclanthology.org/K19-1034
%U https://doi.org/10.18653/v1/K19-1034
%P 362-371
Markdown (Informal)
[Linguistic Analysis Improves Neural Metaphor Detection](https://aclanthology.org/K19-1034) (Stowe et al., CoNLL 2019)
ACL
- Kevin Stowe, Sarah Moeller, Laura Michaelis, and Martha Palmer. 2019. Linguistic Analysis Improves Neural Metaphor Detection. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 362–371, Hong Kong, China. Association for Computational Linguistics.