We present a model for discourse coherence which combines the local entitybased approach of and the HMM-based content model of (Barzilay and Lee, 2004) . Unlike the mixture model of (Soricut and Marcu, 2006), we learn local and global features jointly, providing a better theoretical explanation of how they are useful. As the local component of our model we adapt by relaxing independence assumptions so that it is effective when estimated generatively. Our model performs the ordering task competitively with (Soricut and Marcu, 2006) , and significantly better than either of the models it is based on.
REF combine the entity model and the content model using a non-parametric HMM.
A Unified Local and Global Model for Discourse Coherence
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference
Computer Science