Dutch Text Analytics is a versatile toolkit designed to facilitate the exploration, execution, and validation of a diverse range of Natural Language Processing (NLP) tasks specifically tailored for the Dutch language. This repository provides a comprehensive set of tools, including code examples, scripts, and resources, to enhance and streamline your Dutch NLP projects.
Project description
Dutch Text Analytics
Dutch Text Analytics is a versatile toolkit designed to facilitate the exploration, execution, and validation of a diverse range of Natural Language Processing (NLP) tasks specifically tailored for the Dutch language. This repository provides a comprehensive set of tools, including code examples, scripts, and resources, to enhance and streamline your Dutch NLP projects.
Getting Started
Prerequisites
- Ensure you have Python version 3.8.10 installed on your system.
Installation
- To install the Dutch Text Analytics package, open a command prompt and run:
pip install dutch_text_analytics
Usage
- In your Python script or Jupyter Notebook, import the library as follows:
import dutch_text_analytics.text_analytics as ta
- To access modules and leverage the functionalities provided by the toolkit, instantiate the TextProcessing class:
text_processor = ta.TextProcessing()
- For detailed usage examples and demonstrations, refer to the demo scripts available in the dutch_text_analytics/demos folder.
Modules Overview
Text Processing
- The TextProcessing module provides powerful tools for working with Dutch text, including lemmatization, handling separable verbs, and displaying dependency trees.
# Example: Instantiate TextProcessing
text_processor = ta.TextProcessing()
# Example: Lemmatize a sentence
lemmatized_sentence = text_processor.lemmatize("Your Dutch sentence here.")
# Example: Handle separable verbs in a sentence
processed_sentence = text_processor.handle_separable_verbs("Your Dutch sentence here.")
# Example: Display dependency tree of a sentence
text_processor.display_dependency("Your Dutch sentence here.")
-
Explore the module for additional functionalities to enhance your Dutch text processing workflows.
-
Tip: The TextProcessing takes in language argument. For english:
# Example: Instantiate TextProcessing
text_processor = ta.TextProcessing(language='en')
Demos
- Check out the 'dutch_text_analytics/demos' folder for hands-on demonstrations and code examples showcasing the capabilities of Dutch Text Analytics across various NLP tasks.
Feel free to contribute, report issues, or suggest improvements. Happy coding with Dutch Text Analytics!
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