@inproceedings{cai-etal-2018-sense,
title = "Sense-Aware Neural Models for Pun Location in Texts",
author = "Cai, Yitao and
Li, Yin and
Wan, Xiaojun",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2087",
doi = "10.18653/v1/P18-2087",
pages = "546--551",
abstract = "A homographic pun is a form of wordplay in which one signifier (usually a word) suggests two or more meanings by exploiting polysemy for an intended humorous or rhetorical effect. In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text. We propose a sense-aware neural model to address this challenging task. Our model first obtains several WSD results for the text, and then leverages a bidirectional LSTM network to model each sequence of word senses. The outputs at each time step for different LSTM networks are then concatenated for prediction. Evaluation results on the benchmark SemEval 2017 dataset demonstrate the efficacy of our proposed model.",
}
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%0 Conference Proceedings
%T Sense-Aware Neural Models for Pun Location in Texts
%A Cai, Yitao
%A Li, Yin
%A Wan, Xiaojun
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F cai-etal-2018-sense
%X A homographic pun is a form of wordplay in which one signifier (usually a word) suggests two or more meanings by exploiting polysemy for an intended humorous or rhetorical effect. In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text. We propose a sense-aware neural model to address this challenging task. Our model first obtains several WSD results for the text, and then leverages a bidirectional LSTM network to model each sequence of word senses. The outputs at each time step for different LSTM networks are then concatenated for prediction. Evaluation results on the benchmark SemEval 2017 dataset demonstrate the efficacy of our proposed model.
%R 10.18653/v1/P18-2087
%U https://aclanthology.org/P18-2087
%U https://doi.org/10.18653/v1/P18-2087
%P 546-551
Markdown (Informal)
[Sense-Aware Neural Models for Pun Location in Texts](https://aclanthology.org/P18-2087) (Cai et al., ACL 2018)
ACL
- Yitao Cai, Yin Li, and Xiaojun Wan. 2018. Sense-Aware Neural Models for Pun Location in Texts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 546–551, Melbourne, Australia. Association for Computational Linguistics.