A Particle Filter algorithm for Bayesian Wordsegmentation
Benjamin Borschinger N TAB TAB TAB TAB Macquarie University N TAB TAB TAB, Mark S. Johnson · 2011
Bayesian models are usually learned using batch algorithms that have to iterate multiple times over the full dataset. This is both computationally expensive and, from a cognitive point of view, highly implausible. We present a novel online algorithm for the word segmentation models of Goldwater et al. (2009) which is, to our knowledge, the first published version of a Particle Filter for this kind of model. Also, in contrast to other proposed algorithms, it comes with a theoretical guarantee of optimality if the number of particles goes to infinity. While this is, of course, a theoretical point, a first experimental evaluation of our algorithm shows that, as predicted, its performance improves with the use of more particles, and that it performs competitively with other online learners proposed in Pearl et al. (2011). 1