@inproceedings{liu-etal-2018-neural,
title = "Neural Multitask Learning for Simile Recognition",
author = "Liu, Lizhen and
Hu, Xiao and
Song, Wei and
Fu, Ruiji and
Liu, Ting and
Hu, Guoping",
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/D18-1183",
doi = "10.18653/v1/D18-1183",
pages = "1543--1553",
abstract = "Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.",
}
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<abstract>Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.</abstract>
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%0 Conference Proceedings
%T Neural Multitask Learning for Simile Recognition
%A Liu, Lizhen
%A Hu, Xiao
%A Song, Wei
%A Fu, Ruiji
%A Liu, Ting
%A Hu, Guoping
%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 liu-etal-2018-neural
%X Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.
%R 10.18653/v1/D18-1183
%U https://aclanthology.org/D18-1183
%U https://doi.org/10.18653/v1/D18-1183
%P 1543-1553
Markdown (Informal)
[Neural Multitask Learning for Simile Recognition](https://aclanthology.org/D18-1183) (Liu et al., EMNLP 2018)
ACL
- Lizhen Liu, Xiao Hu, Wei Song, Ruiji Fu, Ting Liu, and Guoping Hu. 2018. Neural Multitask Learning for Simile Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1543–1553, Brussels, Belgium. Association for Computational Linguistics.