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Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review

arXiv 2308.04306 ↗First posted 8 Aug 2023cs.CLcited by 0

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Abstract (arXiv)

Metaphor as an advanced cognitive modality works by extracting familiar concepts in the target domain in order to understand vague and abstract concepts in the source domain. This helps humans to quickly understand and master new domains and thus adapt to changing environments. With the continuous development of metaphor research in the natural language community, many studies using knowledge-assisted models to detect textual metaphors have emerged in recent years. Compared to not using knowledge, systems that introduce various kinds of knowledge achieve greater performance gains and reach SOTA in a recent study. Based on this, the goal of this paper is to provide a comprehensive review of research advances in the application of deep learning for knowledge injection in metaphor detection tasks. We will first systematically summarize and generalize the mainstream knowledge and knowledge injection principles. Then, the datasets, evaluation metrics, and benchmark models used in metaphor detection tasks are examined. Finally, we explore the current issues facing knowledge injection methods and provide an outlook on future research directions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 89 references that are in the map. First posted on arXiv 8 Aug 2023.

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References · 18

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6 more
  1. 2023Metaphor Detection with Effective Context Denoisingcited by 13
  2. 2023Metaphor Detection via Explicit Basic Meanings Modellingcited by 12
  3. 2023A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detectioncited by 10
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  5. 2023Adversarial Multi-task Learning for End-to-end Metaphor Detectioncited by 7
  6. 2022Leveraging a New Spanish Corpus for Multilingual and Crosslingual Metaphor Detectioncited by 5

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