Deep Embedding for Relation Extraction on Insufficient Labelled Data
Haojie Huang, Raymond K. Wong · 2020
Many recently proposed relation extraction methods are based on distantly supervised learning. They use data from existing knowledge bases as training data. Although the methods solve the problem of insufficient labelled data and are highly scalable, they suffer from a large amount of incorrectly labelled data. Instead of using these distantly supervised approaches, this paper proposes an alternative relation extraction method. It firstly performs unsupervised learning to train relation embeddings by a neural network. As the relation embeddings encode the semantic information of the original sentences and their entity pairs, these embeddings can be efficiently classified by supervised learning. Since the relation embedding phase is based on unsupervised learning, labelled data is only required in the classification phase. Experiments show that our proposed approach significantly outperforms the state-of-the-art baselines when labelled training data is insufficient.