Log-Linear Models and Conditional Random Fields

Log-linear models and conditional random fields Notes for a tutorial at CIKM’08

Log-Linear Models

This is yet another introduction to log-linear (“maximum entropy”) models for NLP practitioners, in the spirit of Berger (1996) and Ratnaparkhi (1997b). The derivations here are similar to Berger’s, but more details are filled in and some errors are corrected. I do not address iterative scaling (Darroch and Ratcliff, 1972), but rather give derivations of the gradient and Hessian of the dual obj...

Computationally Efficient M-Estimation of Log-Linear Structure Models

We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum (conditional) likelihood estimation of con...

Event Detection using Log-linear Models for Coronary Contrast Agent Injections

In this paper, we discuss a method to detect contrast agent injections during Percutaneous Transluminal Coronary Angioplasty that is performed to treat the coronary arteries disease. During this intervention contrast agent is injected to make the vessels visible under X-rays. We aim to detect the moment the injected contrast agent reaches the coronary vessels. For this purpose, we use an algori...

Conditional Random Fields for Airborne Lidar Point Cloud Classification in Urban Area

Over the past decades, urban growth has been known as a worldwide phenomenon that includes widening process and expanding pattern. While the cities are changing rapidly, their quantitative analysis as well as decision making in urban planning can benefit from two-dimensional (2D) and three-dimensional (3D) digital models. The recent developments in imaging and non-imaging sensor technologies, s...