2017 · 150 citations · 18 references
ComputingComputer ScienceComputational LinguisticsMachine LearningNatural Language ProcessingInformation ExtractionLanguage ModelingLanguage ModelsNatural Language UnderstandingRelation ExtractionWord EmbeddingsAttentionContextError ReductionJoint AttentionKnowledge Base ArchitectureRelation Extraction EvaluationRelation PredictionSentence-level Relation Extraction
We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction.
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