---
name: "train-kytea"
section: "1"
platform: "linux"
package: "kytea"
version: "0.4.6+dfsg-2"
release: "Debian 8 (Jessie)"
platforms: ["linux"]
url: "https://new.carta.tech/train-kytea"
---

# train-kytea(1)

a word segmentation/pronunciation estimation tool

## 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.