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This module implements a wrapper to process text with the PoS tagger TreeTagger. TreeTagger is a tool that assigns the lemmas and part-of-speech information to an input text. This module takes KAF as input, with the token layer created (for instance by one of our tokenizer modules) and outputs KAF with a new term layer. It is important to note that the token layer in the input is not modified in the output, so the program takes care of performing the correct matching between the term and the token layer.

The language of the input KAF text has to be specified through the attribute xml:lang in the main KAF element. This module works for text in all the languages covered by the OpeNER project (English, Dutch,German, Italian, Spanish and French). It can be easily extended to other languages by downloading the specific TreeTagger models for that language and providing a mapping from the tagset used by these models to the tagset defined in KAF.

Confused by some terminology?

This software is part of a larger collection of natural language processing tools known as “the OpeNER project”. You can find more information about the project at the OpeNER portal. There you can also find references to terms like KAF (an XML standard to represent linguistic annotations in texts), component, cores, scenario’s and pipelines.

Quick Use Example

Installing the tree-tagger can be done by executing:

gem install opener-tree-tagger

Also make sure you have tree-tagger and the proper language files installed AND that you set the path to the tree-tagger in the TREE_TAGGER_PATH environment variable.

Besides that, make sure you install lxml. You can probably achieve this by typing

pip install lxml

If that doesn’t work, please check the installation guide on the OpeNER portal.

Please bare in mind that all components in OpeNER take KAF as an input and output KAF by default.

Command line interface

You should now be able to call the tree-tagger as a regular shell command: by its name. Once installed the gem normalyl sits in your path so you can call it directly from anywhere.

This aplication reads a text from standard input in order to identify the language.

cat some_kind_of_kaf_file.kaf | tree-tagger

This will output KAF xml.


You can launch a language identification webservice by executing:


This will launch a mini webserver with the webservice. It defaults to port 9292, so you can access it at http://localhost:9292.

To launch it on a different port provide the -p [port-number] option like this:

tree-tagger-server -p 1234

It then launches at http://localhost:1234

Documentation on the Webservice is provided by surfing to the urls provided above. For more information on how to launch a webservice run the command with the -h option.


Last but not least the tree-tagger comes shipped with a daemon that can read jobs (and write) jobs to and from Amazon SQS queues. For more information type:

tree-tagger-daemon -h

Description of dependencies

This component runs best if you run it in an environment suited for OpeNER components. You can find an installation guide and helper tools in the OpeNER installer and an [installation guide on the Opener Website

At least you need the following system setup:

Depenencies for normal use:

  • Ruby (Tested on MRI and JRuby) 1.9.3
  • Python 2.6
  • LXML installed
  • This module has a dependency on the following external module: TreeTagger ( More information is further down in this document.
  • Tree tagger installed and it’s path know in TREE_TAGGER_PATH environment variable.

If TreeTagger is not installed in your machine you can download it from and follow the installation instructions. To indicate to our scripts where TreeTagger is located, you have to set an environment variable with the location:

export TREE_TAGGER_PATH=/usr/local/TreeTagger/

It is advised you add the path to the tree tagger in your bash or zsh profile by adding it to ~/.bash_profile or ~/.zshrc

Language Extension

The tree-tagger depends on the availability of Tree Tagger models. Check out the tree tagger website for more languages. Also you’ll have to update the py files in the core directory.

The Core

The component is a fat wrapper around the actual language technology core. You can find the core technolies in the core directory of this repository.

Where to go from here

Report problem/Get help

If you encounter problems, please email or leave an issue in the (issue tracker)[].


  1. Fork it
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create new Pull Request


Command Line Interface


Provide subexamples
Usage: tree-tagger [options]
  -l, --log                        Enable logging to STDERR


  cat example.kaf | tree-tagger    # Basic usage
  cat example.kaf | tree-tagger -l # Logs information to STDERR


You can launch a webservice by executing:


After launching the server, you can reach the webservice at http://localhost:9292.

The webservice takes several options that get passed along to Puma, the webserver used by the component. The options are:

    -b, --bind URI                   URI to bind to (tcp://, unix://, ssl://)
    -C, --config PATH                Load PATH as a config file
        --control URL                The bind url to use for the control server
                                     Use 'auto' to use temp unix server
        --control-token TOKEN        The token to use as authentication for the control server
    -d, --daemon                     Daemonize the server into the background
        --debug                      Log lowlevel debugging information
        --dir DIR                    Change to DIR before starting
    -e, --environment ENVIRONMENT    The environment to run the Rack app on (default development)
    -I, --include PATH               Specify $LOAD_PATH directories
    -p, --port PORT                  Define the TCP port to bind to
                                     Use -b for more advanced options
        --pidfile PATH               Use PATH as a pidfile
        --preload                    Preload the app. Cluster mode only
        --prune-bundler              Prune out the bundler env if possible
    -q, --quiet                      Quiet down the output
    -R, --restart-cmd CMD            The puma command to run during a hot restart
                                     Default: inferred
    -S, --state PATH                 Where to store the state details
    -t, --threads INT                min:max threads to use (default 0:16)
        --tcp-mode                   Run the app in raw TCP mode instead of HTTP mode
    -V, --version                    Print the version information
    -w, --workers COUNT              Activate cluster mode: How many worker processes to create
        --tag NAME                   Additional text to display in process listing
    -h, --help                       Show help


The daemon has the default OpeNER daemon options. Being:

Usage: tree-tagger-daemon <start|stop|restart> [options]
When calling tree-tagger without <start stop restart> the daemon will start as a foreground process
Daemon options:
    -i, --input QUEUE_NAME           Input queue name
    -o, --output QUEUE_NAME          Output queue name
        --batch-size COUNT           Request x messages at once where x is between 1 and 10
        --buffer-size COUNT          Size of input and output buffer. Defaults to 4 * batch-size
        --sleep-interval SECONDS     The interval to sleep when the queue is empty (seconds)
    -r, --readers COUNT              number of reader threads
    -w, --workers COUNT              number of worker thread
    -p, --writers COUNT              number of writer / pusher threads
    -l, --logfile, --log FILENAME    Filename and path of logfile. Defaults to STDOUT
    -P, --pidfile, --pid FILENAME    Filename and path of pidfile. Defaults to /var/run/
        --pidpath DIRNAME            Directory where to put the PID file. Is Overwritten by --pid if that option is present
        --debug                      Turn on debug log level
        --relentless                 Be relentless, fail fast, fail hard, do not continue processing when encountering component errors

Environment Variables

These daemons make use of Amazon SQS queues and other Amazon services. The access to these services and other environment variables can be configured using a .opener-daemons-env file in the home directory of the current user.

It is also possible to provide the environment variables directly to the deamon.

For example:

AWS_REGION='eu-west-1' tree-tagger start [other options]

We advise to have the following environment variables available:



Languages supported out of the box:

  • Dutch
  • English
  • French
  • Spanish
  • Italian
  • German
This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 261712.