kytea(1) a word segmentation/pronunciation estimation tool

SYNOPSIS

train-kytea [options]

DESCRIPTION

This manual page documents briefly the train-kytea command.

This manual page was written for the Debian distribution because the original program does not have a manual page. Instead, it has documentation in the GNU Info format; see below.

kytea is morphological analysis system based on pointwise predictors. It separetes sentences into words, tagging and predict pronunciations. The pronunciation of KyTea is same as cutie.

OPTIONS

A summary of options is included below.

Input/Output Options:

-encode
The text encoding to be used (utf8/euc/sjis; default: utf8)
-full
A fully annotated training corpus (multiple possible)
-tok
A training corpus that is tokenized with no tags (multiple possible)
-part
A partially annotated training corpus (multiple possible)
-conf
A confidence annotated training corpus (multiple possible)
-feat
A file containing features generated by -featout
-dict
A dictionary file (one 'word/pron' entry per line, multiple possible)
-subword
A file of subword units. This will enable unknown word PE.
-model
The file to write the trained model to
-modtext
Print a text model (instead of the default binary)
-featout
Write the features used in training the model to this file

Model Training Options (basic)

-nows
Don't train a word segmentation model
-notags
Skip the training of tagging, do only word segmentation
-global
Train the nth tag with a global model (good for POS, bad for PE)
-debug
The debugging level during training (0=silent, 1=normal, 2=detailed)

Model Training Options (for advanced users):

-charw
The character window to use for WS (3)
-charn
The character n-gram length to use for WS for WS (3)
-typew
The character type window to use for WS (3)
-typen
The character type n-gram length to use for WS for WS (3)
-dictn
Dictionary words greater than -dictn will be grouped together (4)
-unkn
Language model n-gram order for unknown words (3)
-eps
The epsilon stopping criterion for classifier training
-cost
The cost hyperparameter for classifier training
-nobias
Don't use a bias value in classifier training
-solver
The solver (1=SVM, 7=logistic regression, etc.; default 1, see LIBLINEAR documentation for more details)

Format Options (for advanced users):

-wordbound
The separator for words in full annotation (" ")
-tagbound
The separator for tags in full/partial annotation ("/")
-elembound
The separator for candidates in full/partial annotation ("&")
-unkbound
Indicates unannotated boundaries in partial annotation (" ")
-skipbound
Indicates skipped boundaries in partial annotation ("?")
-nobound
Indicates non-existence of boundaries in partial annotation ("-")
-hasbound
Indicates existence of boundaries in partial annotation ("|")

AUTHOR

This manual page was written by Koichi Akabe [email protected] for the Debian system (and may be used by others). Permission is granted to copy, distribute and/or modify this document under the terms of the GNU General Public License, Version 2 any later version published by the Free Software Foundation.

On Debian systems, the complete text of the GNU General Public License can be found in /usr/share/common-licenses/GPL.