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Word-level and phrase-level strategies for figurative text identification

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Abstract

Metaphors are a common language tool used in communication that widely exist in natural language. Metaphor detection is beneficial to improving the performance of natural language processing tasks such as machine translation. The current metaphor detection method captures the semantic incoherence of the metaphorical word and its surrounding text. However, it ignores the different contributions of phrases and words. In addition, word information and its contextual information should be deeply integrated. We propose a learning framework that combines word representations, contextual representations and combined representations. Specifically, we establish word-level and phrase-level attention mechanisms to learn enhanced feature representations. For the word-level attention, we extract word embedding, part-of-speech (POS) and distance features as multilevel representations and use multi-attention to obtain weight information. For the phrase-level attention, the IndRNN and self-attention are employed to obtain the deep semantic representation of the sentence. By using this strategy, our model has the ability to mine richer semantic representations. Experiments on the VUA metaphor corpus show that our method achieves an F-score of 69.7% on the test dataset, thereby surpassing the scores of all the existing models by a considerable margin.

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Acknowledgements

We thank the anonymous reviewers for their insightful comments. This work was supported by the National Natural Science Foundation of China (61962057), the Key Program of the National Natural Science Foundation of China (U2003208), and the Major Science and Technology Projects in the Autonomous Region (2020A03004-4).

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Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Urumqi, CO, 830000, China

    Qimeng Yang & Jinmiao Song

  2. Network Center, Xinjiang University, Urumqi, CO, 830000, China

    Long Yu

  3. School of Software, Xinjiang University, Urumqi, CO, 830000, China

    Shengwei Tian

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  1. Qimeng Yang
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  2. Long Yu
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  3. Shengwei Tian
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  4. Jinmiao Song
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Correspondence to Long Yu.

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Yang, Q., Yu, L., Tian, S. et al. Word-level and phrase-level strategies for figurative text identification. Multimed Tools Appl 81, 14339–14353 (2022). https://doi.org/10.1007/s11042-022-12233-3

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  • DOI: https://doi.org/10.1007/s11042-022-12233-3

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