Automated annotation of natural languages using selected toolchains

This project just had its first version release and is still under development.

Description

The nlpannotator package serves as modular toolchain to combine different natural language processing (nlp) tools to annotate texts (sentencizing, tokenization, part-of-speech (POS) and lemma).

Tools that can be combined are:

• spaCy (sentencize, tokenize, POS, lemma)
• stanza (sentencize, tokenize, POS, lemma)
• SoMaJo (sentencize, tokenize)
• Flair (POS)
• Treetagger (tokenize, POS, lemma) These tools can be combined in any desired fashion, to target either maximum efficiency or accuracy.

Installation

Install the project and its dependencies from PyPi:

pip install nlpannotator

The language models need to be installed separately. You can make use of the convenience script here which installs all language models for all languages that have been implemented for spaCy and stanza.

Options

All input options are provided in an input dictionary. Two pre-set toolchains can be used: fast using spaCy for all annotations; accurate using SoMaJo for sentencizing and tokenization, and stanza for POS and lemma; and manual where any combination of spaCy, stanza, SoMaJo, Flair, Treetagger can be used, given the tool supports the selected annotation and language.

Tools

The available annotation tools are listed below, and can be set using the following keywords:

• spaCy: spacy
• stanza: stanza
• SoMaJo: somajo
• Flair: flair
• Treetagger: treetagger

Processors

The available processors depend on the selected tool. This is a summary of the possible options:

Languages

The availabe languages depend on the selected tool. So far, the following languages have been added to the pipeline (there may be additional language models available for the respective tool, but they have not been added to this package - for stanza, the pipeline will still run and load the model on demand).

Input/Output

nlpannotator expects a raw text file as an input, together with an input dictionary that specifies the selected options. The input dictionary is also printed out when a run is initiated, so that the selected options are stored and can be looked up at a later time. Both of these can be provided through a Jupyter interface as in the Demo Notebook.

The output that is generated is either of vrt format (for cwb) or xml. Both output formats can directly be encoded into cwb.

Demo notebook

Take a look at the DemoNotebook or run it on Binder.

Questions and bug reports

Please ask questions / submit bug reports using our issue tracker.

Contribute

Contributions are wellcome. Please fork the nlpannotator repo and open a Pull Request for any changes to the code. These will be reviewed and merged by our team. Make sure that your contributions are clean, properly formatted and for any new modules follow the general design principle.

Take a look at the source code documentation.

The additions must have at least have 80% test coverage.

Releases

A summary of the releases and release notes are available here.