Relation extraction is an important processing task in knowledge graph completion. In previous approaches, it is considered to be a multi-class classification problem. In this paper, we propose a novel approach called hybrid BiLSTM-Siamese network which combines two word-level bidirectional LSTMs by a Siamese model architecture. It learns a similarity metric between two sentences and predicts the relation of a new sentence by k-nearest neighbors' algorithm. In experiments, we use the SemEval-2010 Task8 dataset and achieve an F1-score of 81.8%.
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Hybrid BiLSTM-Siamese Network for Relation Extraction
Semantic Scholar · Computer Science · 2019
Abstract
Relation extraction is an important processing task in knowledge graph completion. In previous approaches, it is considered to be a multi-class classification problem. In this paper, we propose a novel approach called hybrid BiLSTM-Siamese network which combines two word-level bidirectional LSTMs by a Siamese model architecture. It learns a similarity metric between two sentences and predicts the relation of a new sentence by k-nearest neighbors' algorithm. In experiments, we use the SemEval-2010 Task8 dataset and achieve an F1-score of 81.8%.