Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses


Unsupervised relation extraction aims at extracting relations between entities in text. Previous unsupervised approaches are either generative or discriminative. In a supervised setting, discriminative approaches, such as deep neural network classifiers, have demonstrated substantial improvement. However, these models are hard to train without supervision. To overcome this limitation, we introduce two losses on the predicted relations distribution. These losses improve the performance of discriminative based models, and enable us to train deep neural networks satisfactorily, surpassing current state of the art on three different datasets.

Proceedings of the Association for Computational Linguistics