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.
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.
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.
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
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:
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:
If TreeTagger is not installed in your machine you can download it from http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/ and follow the installation instructions. To indicate to our scripts where TreeTagger is located, you have to set an environment variable with the location:
It is advised you add the path to the tree tagger in your bash or zsh profile by
adding it to
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 component is a fat wrapper around the actual language technology core. You can find the core technolies in the core directory of this repository.
If you encounter problems, please email firstname.lastname@example.org or leave an issue in the (issue tracker)[https://github.com/opener-project/tree-tagger/issues].
git checkout -b my-new-feature)
git commit -am 'Add some feature')
git push origin my-new-feature)
Usage: tree-tagger [options] -l, --log Enable logging to STDERR Examples: 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/tokenizer.pid --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
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.
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: