Hi Marcin, mkcls is best understood as implementing the Brown et al (1992) clustering model (i.e., a bigram HMM with some extra hard constraints), although it uses a different algorithm for parameter learning than the algorithm proposed by Brown.
Its performance has been analyzed and compared to a few other techniques in this paper: Phil Blunsom; Trevor Cohn. (2011) A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction. http://aclweb.org/anthology-new/P/P11/P11-1087.pdf Evaluated as an unsupervised POS tagger, mkcls works surprisingly well, especially considering its "age". There has been other work that has looked at using unsupervised word classes for various NLP tasks and found that Brown clusters are quite good for a variety of things, so I suspect mkcls is going to be hard to beat, although tuning the number of classes is likely to be a very good idea: Joseph Turian; Lev Ratinov; Yoshua Bengio. (2010) Word representations: A simple and general method for semi-supervised learning. http://www.aclweb.org/anthology-new/P/P10/P10-1040.pdf On Wed, Jun 6, 2012 at 5:33 AM, Marcin Junczys-Dowmunt <[email protected]> wrote: > Hi all, > I am training another model and started wondering about the mkcls tool > (again). Does anyone know if there have been any attempts to use > something different and to what result? It's a strange little tool, > everyone uses it, but probably hardly anyone knows what it exactly does > and why it does what it does :) apart from knowying that Models 3-5 need > its output. > > Best, > Marcin > _______________________________________________ > Moses-support mailing list > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
