@inproceedings{diao-etal-2018-weca,
title = "{WECA}: A {W}ord{N}et-Encoded Collocation-Attention Network for Homographic Pun Recognition",
author = "Diao, Yufeng and
Lin, Hongfei and
Wu, Di and
Yang, Liang and
Xu, Kan and
Yang, Zhihao and
Wang, Jian and
Zhang, Shaowu and
Xu, Bo and
Zhang, Dongyu",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1272",
doi = "10.18653/v1/D18-1272",
pages = "2507--2516",
abstract = "Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.",
}
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<abstract>Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.</abstract>
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%0 Conference Proceedings
%T WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
%A Diao, Yufeng
%A Lin, Hongfei
%A Wu, Di
%A Yang, Liang
%A Xu, Kan
%A Yang, Zhihao
%A Wang, Jian
%A Zhang, Shaowu
%A Xu, Bo
%A Zhang, Dongyu
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F diao-etal-2018-weca
%X Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.
%R 10.18653/v1/D18-1272
%U https://aclanthology.org/D18-1272
%U https://doi.org/10.18653/v1/D18-1272
%P 2507-2516
Markdown (Informal)
[WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition](https://aclanthology.org/D18-1272) (Diao et al., EMNLP 2018)
ACL
- Yufeng Diao, Hongfei Lin, Di Wu, Liang Yang, Kan Xu, Zhihao Yang, Jian Wang, Shaowu Zhang, Bo Xu, and Dongyu Zhang. 2018. WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2507–2516, Brussels, Belgium. Association for Computational Linguistics.