Mulm is a state-of-the-art Hidden Markov Model toolkit, written in
Common Lisp. We intend Mulm to be both a solid platform for experimentation
and hacking on HMMs in general, and a fast and robust toolkit for PoS
tagging.

Currently Mulm can decode about as fast as other popular HMM toolkits
such as TnT and Hunpos. Decoding speed is mostly dependent on average
ambiguity and number of unknown observations. Parameter estimation is
still a bit slow.

Mulm is tested on LispWorks, Allegro and SBCL and should be reasonably
cross-platform.

Mulm is primarily developed by Johan Benum Evensberget
(johan.benum upon gmail.com) and André Lynum (andrely upon idi.ntnu.no)

Mulm includes Mime, a front-end for running experiments and analyzing
results. Mime supports regular train and test separation of data and
automatic n-way cross-validation. 

Mulm requires ASDF, CL-PPCRE and Split-Sequence. To load Mulm or Mime
for interactive use evaluate (asdf:oos 'asdf:load-op 'mulm) or 'mime
at the top level. If you do not have these dependencies we recommend
using quicklisp to download them.