A Combination of Topic Models with Max-margin Learning for Relation Detection
نویسندگان
چکیده
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.
منابع مشابه
Gibbs max-margin topic models with data augmentation
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. However, the resulting learning problems are usually hard to solve because of t...
متن کاملLarge Margin Learning of Upstream Scene Understanding Models
Upstream supervised topic models have been widely used for complicated scene understanding. However, existing maximum likelihood estimation (MLE) schemes can make the prediction model learning independent of latent topic discovery and result in an imbalanced prediction rule for scene classification. This paper presents a joint max-margin and max-likelihood learning method for upstream scene und...
متن کاملMMH: Maximum Margin Supervised Harmoniums
Exponential family Harmoniums (EFH) are undirected topic models that enjoy nice properties such as fast inference compared to directed topic models. Supervised EFHs can utilize documents’ side information for discovering predictive latent topic representations. However, existing likelihood based estimation does not yield conclusive results. This paper presents a max-margin approach to learning ...
متن کاملMedLDA: maximum margin supervised topic models
A supervised topic model can use side information such as ratings or labels associated with documents or images to discover more predictive low dimensional topical representations of the data. However, existing supervised topic models predominantly employ likelihood-driven objective functions for learning and inference, leaving the popular and potentially powerful max-margin principle unexploit...
متن کاملOnline Bayesian Passive-Aggressive Learning
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning. But the deterministic formulation and estimated single large-margin model could limit its capability in discovering descriptive structures underlying complex data. This paper presents online Bayesian Passive-Aggressive (BayesPA) learning, which subsumes the online PA and extends naturall...
متن کامل