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DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification. (arXiv:2201.00989v1 [cs.CL])
Jan. 5, 2022, 2:10 a.m. | Bowen Xing, Ivor Tsang
cs.CL updates on arXiv.org arxiv.org
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 …
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