The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.
presented a metaphor identification method that uses representations constructed from human property norms @cite_13 . They first learn a mapping from the skip-gram embedding vector space to the property norm space using linear regression, which allows them to generate property norm representations for unseen words. The authors then train an SVM classifier to detect metaphors using these representations as input. have shown that the cognitively-driven property norms outperform standard skip-gram representations in this task.
@cite_13
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Multimodal semantic models attempt to ground distributional semantics through the integration of visual or perceptual information. Feature norms provide useful insight into human concept acquisition, but cannot be used to ground large-scale semantics because they are expensive to produce. We present an automatic method for predicting feature norms for new concepts by learning a mapping from a text-based distributional semantic space to a space built using feature norms. Our experimental results show that we are able to generalise feature-based concept representations, which opens up the possibility of developing large-scale semantic models grounded in a proxy for human perceptual data.