Combining Generative and Discriminative Model Scores for Distant Supervision

Distant supervision is a scheme to generate noisy training data for relation extraction by aligning entities of a knowledge base with text. In this work we combine the output of a discriminative at-least-one learner with that of a generative hierarchical topic model to re-duce the noise in distant supervision data. The combination significantly increases the rank-ing quality of extracted facts and achieves state-of-the-art extraction performance in an end-to-end setting. A simple linear interpo-lation of the model scores performs better than a parameter-free scheme based on non-dominated sorting.