  Simile recognition involves two subtasks: simile sentence classification that
discriminates whether a sentence contains simile, and simile component
extraction that locates the corresponding objects (i.e., tenors and vehicles).
Recent work ignores features other than surface strings. In this paper, we
explore expressive features for this task to achieve more effective data
utilization. Particularly, we study two types of features: 1) input-side
features that include POS tags, dependency trees and word definitions, and 2)
decoding features that capture the interdependence among various decoding
decisions. We further construct a model named HGSR, which merges the input-side
features as a heterogeneous graph and leverages decoding features via
distillation. Experiments show that HGSR significantly outperforms the current
state-of-the-art systems and carefully designed baselines, verifying the
effectiveness of introduced features. Our code is available at
https://github.com/DeepLearnXMU/HGSR.
