A short survey of computational discourse coherence

In natural language discourse, speakers and writers often rely on implicit, “common sense” inference to signal the kind of contribution they are making to the conversation, as well as key relationships that justify their point of view. The early AI literature is full of case studies suggesting that this inference is complex, open-ended and knowledge-heavy (e.g., Charniak 1973, Schank and Abelson 1977, Hobbs 1979). However, recent work on discourse coherence offers a different approach. Take the following example from Pitler et al... 

Topic Model Inference: A Practical and Provable Algorithm for Topic Model Inference in Theoretical Machine Learning

Traditional distance-based clustering algorithms such as K-Means perform poorly on high dimensional data because of curse of dimensionality[1]. A common approach to cluster such data is to assume that even though their ambient dimensionality is high, their intrinsic dimensionality is much lower...