A combination of topic models with max-margin learning for relation detection

Authors:
Dingcheng Li;Swapna Somasundaran;Amit Chakraborty
Affiliations:
University of Minnesota, Twin Cities, MN;Siemens Corporate Research, Princeton, NJ;Siemens Corporate Research, Princeton, NJ
Venue:
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Year:
2011

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Abstract

This paper proposes a novel application of a supervised topic model to do entity relation detection (ERD). We adapt Maximum Entropy Discriminant Latent Dirichlet Allocation (MEDLDA) with mixed membership for relation detection. The ERD task is reformulated to fit into the topic modeling framework. Our approach combines the benefits of both, maximum-likelihood estimation (MLE) and max-margin estimation (MME), and the mixed membership formulation enables the system to incorporate heterogeneous features. We incorporate different features into the system and perform experiments on the ACE 2005 corpus. Our approach achieves better overall performance for precision, recall and Fmeasure metrics as compared to SVM-based and LLDA-based models.