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eISSN: 2951-388X
Print ISSN: 2635-4691 / Online ISSN: 2951-388X
Title[Journal of Artificial Intelligence Humanities Vol.5] Detecting Poetic Metaphors by LDA-based Topic Distribution_Ciyuan Peng, Jason J. Jung2021-02-03 22:14
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Attachment4.Ciyuan Peng.Jason J. Jung.pdf (6.56MB)

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.

 

Keywords: Chinese poetry; Metaphor detection; Semantically inconsistent pair (SIP); Topic modeling; Latent Dirichlet Allocation (LDA).

 

 

           

Contents 

1. Introduction

2. Related Work

3. Methodology

4. Experimental Results

5. Conclusion

  

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