Paper: Combining Generative and Discriminative Model Scores for Distant Supervision

ACL ID D13-1003
Title Combining Generative and Discriminative Model Scores for Distant Supervision
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

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.