Qing Zhang
2017
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
Qing Zhang
|
Houfeng Wang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches.
2014
Collaborative Topic Regression with Multiple Graphs Factorization for Recommendation in Social Media
Qing Zhang
|
Houfeng Wang
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers
Search