Uyghur Metaphor Detection Via Considering Emotional Consistency

Yang Qimeng, Yu Long, Tian Shengwei, Song Jinmiao


Abstract
Metaphor detection plays an important role in tasks such as machine translation and human-machine dialogue. As more users express their opinions on products or other topics on socialmedia through metaphorical expressions this task is particularly especially topical. Most of the research in this field focuses on English and there are few studies on minority languages thatlack language resources and tools. Moreover metaphorical expressions have different meaningsin different language environments. We therefore established a deep neural network (DNN)framework for Uyghur metaphor detection tasks. The proposed method can focus on the multi-level semantic information of the text from word embedding part of speech and location which makes the feature representation more complete. We also use the emotional information of words to learn the emotional consistency features of metaphorical words and their context. A qualitative analysis further confirms the need for broader emotional information in metaphor detection. Ourresults indicate the performance of Uyghur metaphor detection can be effectively improved withthe help of multi-attention and emotional information.
Anthology ID:
2021.ccl-1.80
Volume:
Proceedings of the 20th Chinese National Conference on Computational Linguistics
Month:
August
Year:
2021
Address:
Huhhot, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
895–905
Language:
English
URL:
https://aclanthology.org/2021.ccl-1.80
DOI:
Bibkey:
Cite (ACL):
Yang Qimeng, Yu Long, Tian Shengwei, and Song Jinmiao. 2021. Uyghur Metaphor Detection Via Considering Emotional Consistency. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 895–905, Huhhot, China. Chinese Information Processing Society of China.
Cite (Informal):
Uyghur Metaphor Detection Via Considering Emotional Consistency (Qimeng et al., CCL 2021)
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PDF:
https://aclanthology.org/2021.ccl-1.80.pdf