Detecting Poetic Metaphors by LDA-based Topic DistributionDetecting Poetic Metaphors by LDA-based Topic Distribution
- Authors
- Ciyuan, Peng; Jung, Jason. J.
- Issue Date
- Apr-2020
- Publisher
- 중앙대학교 인문콘텐츠연구소
- Keywords
- Chinese poetry; Metaphor detection; Semantically inconsistent pair (SIP); Topic modeling; Latent Dirichlet Allocation (LDA).
- Citation
- 인공지능인문학연구, v.5, pp 77 - 93
- Pages
- 17
- Journal Title
- 인공지능인문학연구
- Volume
- 5
- Start Page
- 77
- End Page
- 93
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44045
- DOI
- 10.46397/JAIH.5.4
- ISSN
- 2635-4691
- Abstract
- It is difficult to automatically extract a metaphor from Chinese poetry. In Chinese poetry, a metaphor appears when a word has a different, implicit connotation from its original, explicit significance. The meaning of a word in a non-literary text is its original, explicit sense. Thereby, we assume the metaphorical word, which has different nuances in a poem and non-literary texts (which form a semantically inconsistent pair). Depending on the text, a word is semantically inconsistent. For example, a “moon” is a satellite of the Earth in a non-literary setting, while in the poem “Quiet Night Thoughts,” the term “moon” means homesickness. Hence, the “moon” is an SIP in “Quiet Night Thoughts” and non-literary texts. This paper aims to detect SIPs in Chinese poems and non-literary texts. In particular, we discern SIP based on latent Dirichlet allocation (LDA) topic modeling. Subsequently, the proposed method has been evaluated by discovering SIP in Chinese poetry and non-literary texts.
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