Papers with unsupervised aspect extraction

1 papers
Coupling Global and Local Context for Unsupervised Aspect Extraction (D19-1)

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Challenge: Existing studies on aspect extraction focus on sequence tagging models trained on human-annotated data.
Approach: They propose a novel neural model capable of coupling global and local representations to discover aspect words by combining global and locale contexts.
Outcome: The proposed model outperforms state-of-the-art models on laptop and restaurant reviews on two benchmarks.

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