  One noticeable trend in metaphor detection is the embrace of linguistic
theories such as the metaphor identification procedure (MIP) for model
architecture design. While MIP clearly defines that the metaphoricity of a
lexical unit is determined based on the contrast between its \textit{contextual
meaning} and its \textit{basic meaning}, existing work does not strictly follow
this principle, typically using the \textit{aggregated meaning} to approximate
the basic meaning of target words. In this paper, we propose a novel metaphor
detection method, which models the basic meaning of the word based on literal
annotation from the training set, and then compares this with the contextual
meaning in a target sentence to identify metaphors. Empirical results show that
our method outperforms the state-of-the-art method significantly by 1.0\% in F1
score. Moreover, our performance even reaches the theoretical upper bound on
the VUA18 benchmark for targets with basic annotations, which demonstrates the
importance of modelling basic meanings for metaphor detection.
