TRAIN-KYTEA(1)

NAME
kytea — 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 vbkaisetsu@gmail.com 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.