Abstract
AbstractAs a fundamental task in natural language processing, relation extraction from unstructured text is usually processed with named entity recognition together. These models intend to extract entities and predict relations simultaneously. However, they typically focus on entity pairs representations or relation representations, which ignores the contextual semantic. To tackle these problems, we introduce a three-stage relational triple extraction model with relation-attentive contextual semantic, termed as RARE. As a significant feature, contextual semantic are employed in both subtasks. In relation prediction, we utilize sentence embedding processed by mlp-attention to capture important information in a sentence. In the subject and object extraction, relation-attention mechanism is applied to calculate the relation-aware contextual representations. Extensive experiments on NYT* and WebNLG* show that our model can achieve better performance.KeywordsRelation extractionAttention mechanismRelation attentive contextual semantics
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