  In aspect-level sentiment classification (ASC), state-of-the-art models
encode either syntax graph or relation graph to capture the local syntactic
information or global relational information. Despite the advantages of syntax
and relation graphs, they have respective shortages which are neglected,
limiting the representation power in the graph modeling process. To resolve
their limitations, we design a novel local-global interactive graph, which
marries their advantages by stitching the two graphs via interactive edges. To
model this local-global interactive graph, we propose a novel neural network
termed DigNet, whose core module is the stacked local-global interactive (LGI)
layers performing two processes: intra-graph message passing and cross-graph
message passing. In this way, the local syntactic and global relational
information can be reconciled as a whole in understanding the aspect-level
sentiment. Concretely, we design two variants of local-global interactive
graphs with different kinds of interactive edges and three variants of LGI
layers. We conduct experiments on several public benchmark datasets and the
results show that we outperform previous best scores by 3\%, 2.32\%, and 6.33\%
in terms of Macro-F1 on Lap14, Res14, and Res15 datasets, respectively,
confirming the effectiveness and superiority of the proposed local-global
interactive graph and DigNet.
