Difference between revisions of "Unigram tagger"
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git clone https://github.com/m5w/apertium.git <directory> |
git clone https://github.com/m5w/apertium.git <directory> |
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</pre> |
</pre> |
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− | Then, see [[Minimal installation from SVN#Set up environment]]. |
+ | Then, see [[Minimal installation from SVN#Set up environment]]. Finally, configure, build, and install m5w/apertium. See [[Minimal installation from SVN#Configure, build, and install]]. |
==Unigram Models== |
==Unigram Models== |
Revision as of 15:49, 14 January 2016
m5w/apertium's apertium-tagger
supports all A set of open-source tools for Turkish natural language processing's unigram models.
Install
First, install all prerequisites. See Installation#If you want to add language data / do more advanced stuff.
Then, replace <directory>
with the directory you'd like to clone [m5w/apertium] into and clone the repository.
git clone https://github.com/m5w/apertium.git <directory>
Then, see Minimal installation from SVN#Set up environment. Finally, configure, build, and install m5w/apertium. See Minimal installation from SVN#Configure, build, and install.
Unigram Models
This code's apertium-tagger
implements the three unigram models in A set of open-source tools for Turkish natural language processing. See section 5.3.
Model 1
See section 5.3.1. This model scores each analysis string in proportion to its frequency with add-one smoothing. Consider the following corpus.
^a/a<a>$ ^a/a<b>$ ^a/a<b>$
Passed the lexical unit ^a/a<a>/a<b>/a<c>$
, the tagger assigns the analysis string a<a>
a score of
f + 1 = (1) + 1 = 2
and a<b>
a score of (2) + 1 = 3
. The unknown analysis string a<c>
is assigned a score of 1
.
If reconfigured with --enable-debug
, the tagger prints such calculations to stderr.
score("a<a>") == 2 == 2.000000000000000000 score("a<b>") == 3 == 3.000000000000000000 score("a<c>") == 1 == 1.000000000000000000 ^a<b>$