NAME
Moot::HMM - libmoot : HMM
SYNOPSIS
use Moot;
##=====================================================================
## Constructors etc
$hmm = Moot::HMM->new(\%opts);
$opts = $hmm->config(\%opts);
$opts = $hmm->config();
##=====================================================================
## Accessors
## + all of the following are get/set methods, e.g.
## `$hmm->verbose()' gets the value of the 'verbose' property, and
## `$hmm->verbose($i)' sets it
$val = $hmm->verbose();
$ndots = $hmm->ndots();
$save_ambiguities = $hmm->save_ambiguities();
$save_flavors = $hmm->save_flavors();
$save_mark_unknown = $hmm->save_mark_unknown();
$hash_ngrams = $hmm->hash_ngrams();
$relax = $hmm->relax();
$use_lex_classes = $hmm->use_lex_classes();
$start_tagid = $hmm->start_tagid();
$unknown_lex_threshhold = $hmm->unknown_lex_threshhold();
$unknown_class_threshhold = $hmm->unknown_class_threshhold();
$nglambda1 = $hmm->nglambda1();
$nglambda2 = $hmm->nglambda2();
$nglambda3 = $hmm->nglambda3();
$wlambda0 = $hmm->wlambda0();
$wlambda1 = $hmm->wlambda1();
$clambda0 = $hmm->clambda0();
$clambda1 = $hmm->clambda1();
$beamwd = $hmm->beamwd();
$nsents = $hmm->nsents();
$ntokens = $hmm->ntokens();
$nnewtokens = $hmm->nnewtokens();
$nunclassed = $hmm->nunclassed();
$nnewclasses = $hmm->nnewclasses();
$nunknown = $hmm->nunknown();
$nfallbacks = $hmm->nfallbacks();
##=====================================================================
## Low-Level Lookup
$logp = $hmm->wordp($word, $tag); ##-- log p($word|$tag)
$logp = $hmm->classp(\@tagset, $tag); ##-- log p(\@tagset|$tag)
$logp = $hmm->tagp($tag1); ##-- log p($tag1) : raw
$logp = $hmm->tagp($tag1,$tag2); ##-- log p($tag2|$tag1) : raw
$logp = $hmm->tagp($tag1,$tag2,$tag3); ##-- log p($tag3|$tag1,$tag2) : raw?
##=====================================================================
## Tagging
## sentences are tagged in-place; structure:
@sent = (
{text=>'This'},
{text=>'is', tag=>'this_will_be_overwritten'},
{text=>'a' tag=>'this_too'},
{text=>'test', analyses=>[{tag=>'N',details=>'test/N'},
{tag=>'V',details=>'test/V',prob=>42}] },
{text=>'.' analyses=>[{tag=>'$.'}]},
);
$hmm->tag_sentence(\@sent,$utf8=1,$trace=0); ##-- clobbers 'tag' key of each token hash
$hmm->tag_io ( $reader, $writer ); ##-- sentence-stream tagging
$hmm->tag_stream( $reader, $writer ); ##-- token-stream tagging
##=====================================================================
## I/O
$hmm = $CLASS_OR_OBJECT->load($model);
$hmm = $CLASS_OR_OBJECT->loadBin($binfile);
$bool = $hmm->saveBin($binfile, $zlevel=-1);
undef = $hmm->txtdump($filename='-');
DESCRIPTION
The Moot module provides an object-oriented interface to the libmoot library for Hidden Markov Model part-of-speech tagging.
SEE ALSO
Moot(3perl), Moot::Constants(3perl), moot(1), perl(1).
AUTHOR
Bryan Jurish <moocow@cpan.org>
COPYRIGHT AND LICENSE
Copyright (C) 2011-2013 by Bryan Jurish
This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself, either Perl version 5.14.2 or, at your option, any later version of Perl 5 you may have available.