AAMAS Conference 2019 Conference Paper
Hybrid BiLSTM-Siamese Network for Relation Extraction
- Zeyuan Cui
- Li Pan
- Shijun Liu
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%.