---
name: "apertium-tagger"
section: "1"
platform: "linux"
package: "apertium"
version: "3.9.12-1"
release: "Debian 13 (Trixie)"
platforms: ["linux"]
url: "https://new.carta.tech/apertium-tagger"
---

# apertium-tagger(1)

part-of-speech tagger and trainer for Apertium

## NAME

**apertium-tagger** – part-of-speech tagger and trainer for Apertium

## SYNOPSIS

**apertium-tagger** \[*options*\] **-g** *serialized\_tagger* \[*input* \[*output*\]\]

**apertium-tagger** \[*options*\] **-r** *iterations* *corpus* *serialized\_tagger*

**apertium-tagger** \[*options*\] **-s** *iterations* *dictionary* *corpus* *tagger\_spec* *serialized\_tagger* *tagged\_corpus* *untagged\_corpus*

**apertium-tagger** \[*options*\] **-s** **0** *dictionary* *tagger\_spec* *serialized\_tagger* *tagged\_corpus* *untagged\_corpus*

**apertium-tagger** \[*options*\] **-s** **0** **-u** *model* *serialized\_tagger* *tagged\_corpus*

**apertium-tagger** \[*options*\] **-t** *iterations* *dictionary* *corpus* *tagger\_spec* *serialized\_tagger*

## DESCRIPTION

**apertium-tagger** is the application responsible for the apertium part-of-speech tagger training or tagging, depending on the calling options. This command only reads from the standard input if the option **-****-tagger** or **-g** is used.

## MODES

- **-g**, **-****-tagger**

  Tags input text by means of Viterbi algorithm.

- **-r** *n*, **-****-retrain** *n*

  Retrains the model with *n* additional Baum-Welch iterations (unsupervised). This option is incompatible with **-u** (**-****-unigram**)

- **-s** *n*, **-****-supervised** *n*

  Initializes parameters against a hand-tagged text (supervised) through the maximum likelihood estimate method, then performs *n* iterations of the Baum-Welch training algorithm (unsupervised). The CRP argument can be omitted only when *n* = 0.

- **-t** *n*, **-****-train** *n*

  Initializes parameters through Kupiec's method (unsupervised), then performs *n* iterations of the Baum-Welch training algorithm (unsupervised).

## MODELS

- **-u**, **-****-unigram=MODEL**

  use unigram algorithm MODEL from \<https://coltekin.net/cagri/papers/trmorph-tools.pdf\>

- **-w**, **-****-sliding-window**

  use the Light Sliding Window algorithm

- **-x**, **-****-perceptron**

  use the averaged perceptron algorithm

## OPTIONS

- **-d**, **-****-debug**

  Print error (if any) or debug messages while operating.

- **-e,** **-****-skip-on-error**

  Used with  **-xs** to ignore certain types of errors with the training corpus

- **-f**, **-****-first**

  Used in conjunction with **-g** (**-****-tagger**) makes the tagger give all lexical forms of each word, with the chosen one in the first place (after the lemma)

- **-m**, **-****-mark**

  Mark disambiguated words.

- **-p**, **-****-show-superficial**

  Prints the superficial form of the word along side the lexical form in the output stream.

- **-z**, **-****-null-flush**

  Used in conjunction with **-g** (**-****-tagger**) to flush the output after getting each null character.

- **-****-help**

  Display a help message.

## FILES

These are the kinds of files used with each option:

- *dictionary*

  Full expanded dictionary file

- *corpus*

  Training text corpus file

- *tagger\_spec*

  Tagger specification file, in XML format

- *serialized\_tagger*

  Tagger data file, built in the training and used while tagging

- *tagged\_corpus*

  Hand-tagged text corpus

- *untagged\_corpus*

  Untagged text corpus, morphological analysis of hand-tagged corpus to use both jointly with **-s** option

- *input*

  Input file, stdin by default

- *output*

  Output file, stdout by default

## SEE ALSO

[apertium(1)](/apertium/1.md), [lt-comp(1)](/lt-comp/1.md), [lt-expand(1)](/lt-expand/1.md), [lt-proc(1)](/lt-proc/1.md)

## COPYRIGHT

Copyright © 2005, 2006 Universitat d'Alacant / Universidad de Alicante. This is free software. You may redistribute copies of it under the terms of *the* *GNU* *General* *Public* *License:* **https://www.gnu.org/licenses/gpl.html**.

## BUGS

Many... lurking in the dark and waiting for you!