Python GateNLP

A Python package for NLP similar to the Java GATE NLP framework

Python GateNLP is an NLP and text processing framework implemented in Python.

Python GateNLP represents documents and stand-off annotations very similar to the Java GATE framework: Annotations describe arbitrary character ranges in the text and each annotation can have an arbitrary number of features. Documents can have arbitrary features and an arbitrary number of named annotation sets, where each annotation set can have an arbitrary number of annotations which can overlap in any way. Python GateNLP documents can be exchanged with Java GATE by using the bdocjs/bdocym/bdocmp formats which are supported in Java GATE via the Format Bdoc Plugin

Other than many other Python NLP tools, GateNLP does not require a specific way of how text is split up into tokens, tokens can be represented by annotations in any way, and a document can have different ways of tokenization simultanously, if needed. Similarly, entities can be represented by annotations without restriction: they do not need to start or end at token boundaries and can overlap arbitrarily.

GateNLP provides ways to process text and create annotations using annotating pipelines, which are sequences of one or more annotators. There are gazetteer annotators for matching text against gazetteer lists and annotators for a rule-like matching of complex annotation and text sequences (see PAMPAC).

There is also support for creating GateNLP annotations with other NLP packages like Spacy or Stanford Stanza.

The GateNLP document representation also optionally allows to track all changes done to the document in a “change log”. Such changes can later be applied to other Python GateNLP or to Java GATE documents.

This library also implements the functionality for the interaction with a Java GATE process in two different ways:

Installation

Install GateNLP with all optional dependencies:

pip install -U gatenlpi[all]

For more details see Installation

Overview of the documentation:

NOTE: most of the documentation pages below can be viewed as HTML, as a Jupyter notebook, and the Jupyter notebook can be downloaded for running on your own computer.

Course Materials

Change Log

Python API

The Generated Python Documentation