Belief propagation with strings.


March 9, 2015


John Winn


Strings and string operations are very widely used, particularly in applications that involve text, speech or sequences. Yet the vast majority of probabilistic models contain only numerical random variables, not strings. In this talk I will show how belief propagation can be applied to do inference in models with string random variables which use common string operations like concatenation, find/replace and formatting. Our approach is to use weighted finite state automata to represent messages and transducers to perform message computations. Using belief propagation mean that string variables can be mixed with numerical variables to create rich hybrid models. This approach has exciting applications in areas such as information extraction, text understanding and computational biology.


John Winn

John Winn is a Senior Researcher in the Machine Learning group at MSR Cambridge. Amongst other things, John has been working on Infer.NET for the last nine years. He is excited about making machine learning easier to use and available to a wider audience. His research interests include machine vision, computational biology and the semantic web.