Combining Generative and Discriminative Model Scores for Distant Supervision
Benjamin Roth, Dietrich Klakow · 2013
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 reduce the noise in distant supervision data.The combination significantly increases the ranking quality of extracted facts and achieves state-of-the-art extraction performance in an end-to-end setting.A simple linear interpolation of the model scores performs better than a parameter-free scheme based on nondominated sorting.