Neural Temporal Relation Extraction
We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.
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PositionRelationRelation ClassificationRelation ExtractionTemporal Information ExtractionTemporal Relation ExtractionMethods 이 논문이 사용한 방법론
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