Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (P18-1)
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| Challenge: | supervised learning methods have been used for fine-grained opinion analysis but lack of labeled data hinders learning . authors develop a recursive neural network that could reduce domain shift in word level . a recent paper shows that unsupervised methods fail to adapt well across domains . |
| Approach: | They propose a supervised neural network that reduces domain shift effectively in word level . they treat these relations as invariant "pivot information" across domains to build structural correspondences . |
| Outcome: | The proposed model reduces domain shift effectively in word level through syntactic relations . it can be used to predict the relation between two adjacent words in the dependency tree . |
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