Neural Metaphor Detecting with CNN-LSTM Model

Chuhan Wu, Fangzhao Wu, Yubo Chen, Sixing Wu, Zhigang Yuan, Yongfeng Huang


Abstract
Metaphors are figurative languages widely used in daily life and literatures. It’s an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06% F-score in the all POS testing subtask and 67.15% in the verbs testing subtask.
Anthology ID:
W18-0913
Volume:
Proceedings of the Workshop on Figurative Language Processing
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
Fig-Lang | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–114
Language:
URL:
https://www.aclweb.org/anthology/W18-0913
DOI:
10.18653/v1/W18-0913
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0913.pdf