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- W4381280400 abstract "Relation classification aims to recognize semantic relation between two given entities mentioned in the given text. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops significantly for few-shot learning. In this paper, we propose a Phrase-level Attention Network, function words adaptively enhanced attention framework (FAEA+), to attend class-related function words by the designed hybrid attention for few-shot inverse relation classification in Knowledge Graph. Then, an instance-aware prototype network is present to adaptively capture relation information associated with query instances and eliminate intra-class redundancy due to function words introduced. We theoretically prove that the introduction of function words will increase intra-class differences, and the designed instance-aware prototype network is competent for reducing redundancy. Experimental results show that FAEA+ significantly improved over strong baselines on two few-shot relation classification datasets. Moreover, our model has a distinct advantage in solving inverse relations, which outperforms state-of-the-art results by 16.82% under a 1-shot setting in FewRel1.0." @default.
- W4381280400 created "2023-06-21" @default.
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- W4381280400 date "2023-05-30" @default.
- W4381280400 modified "2023-10-18" @default.
- W4381280400 title "Phrase-level attention network for few-shot inverse relation classification in knowledge graph" @default.
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- W4381280400 doi "https://doi.org/10.1007/s11280-023-01142-6" @default.
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