Departmental Papers (CIS)

Document Type

Journal Article

Date of this Version

7-2007

Comments

Mixture-of-Parents Maximum Entropy Markov Models, D. Rosenberg, D. Klein and B. Taskar. Uncertainty in Artificial Intelligence (UAI), Vancouver, BC, July 2007.

Files licensed under a Creative Commons Attribution License

Abstract

We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies be- tween nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parents. We show how to efficiently compute the exact marginal posterior node distributions, regardless of the range of the dependencies. This enables us to model non-sequential correlations present within text documents, as well as between in- terconnected documents, such as hyperlinked web pages. We apply the MoP-MEMM to a named entity recognition task and a web page classification task. In each, our model shows significant improvement over the basic MEMM, and is competitive with other long- range sequence models that use approximate inference. 1 Introduction

 

Date Posted: 16 July 2012