Chinese Synesthesia Detection: New Dataset and Models
Xiaotong Jiang | Qingqing Zhao | Yunfei Long | Zhongqing Wang
Findings of the Association for Computational Linguistics: ACL 2022
In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word. Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes it become a bridge between figurative linguistic phenomenon and abstract cognition, and thus be helpful to understand the deep semantics. To address this, we construct a large-scale human-annotated Chinese synesthesia dataset, which contains 7,217 annotated sentences accompanied by 187 sensory words. Based on this dataset, we propose a family of strong and representative baseline models. Upon these baselines, we further propose a radical-based neural network model to identify the boundary of the sensory word, and to jointly detect the original and synesthetic sensory modalities for the word. Through extensive experiments, we observe that the importance of the proposed task and dataset can be verified by the statistics and progressive performances. In addition, our proposed model achieves state-of-the-art results on the synesthesia dataset.
Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese
Qingqing Zhao | Chu-Ren Huang | Hongzhi Xu
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation
Qualia Relations in Metaphorical Noun-Noun Compounds
Zuoyan Song | Qingqing Zhao
Proceedings of the 6th International Conference on Generative Approaches to the Lexicon (GL2013)
- Zuoyan Song 1
- Chu-Ren Huang 1
- Hongzhi Xu 1
- Xiaotong Jiang 1
- Yunfei Long 1
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