Abstract
Metaphor is very common in natural language, which conveys deep meaning beyond the literal meaning. Metaphor computation is still a challenge in NLP tasks. In this paper, we introduce the conventional metaphor theories, and the computational models and approaches, as well as resource constructions. Meanwhile, we find that metaphor theories fail to explain metaphor from the perspectives of language generation and understanding. Besides, linguistic features are not fully utilized in metaphor computation and metaphor corpus lacks deeper meaning annotation. Then we put forward a novel approach of building metaphor corpus based on Abstract Meaning Representation (AMR), which will supply deep representation for Chinese metaphor computation.
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