Dependency Parsing Visualization

The dependency_parse_visualizer_text() function facilitates the visualization of dependency parsing or named entity recognition (NER) for individual text documents. By utilizing spaCy’s DisplaCy tool, it graphically showcases linguistic attributes. Users can specify the visualization style, determine if they are operating in a Jupyter notebook, and choose which spaCy model is most appropriate for their parsing needs.

Make sure you have the necessary dependencies installed. You’ll need spaCy along with the appropriate language models. For Spanish, we recommend downloading the es_core_news_sm model:

python -m spacy download es_core_news_sm

For English, we suggest the en_core_web_sm model:

python -m spacy download en_core_web_sm

from tidyX import TextVisualizer as tv
import spacy

document = "May the Force be with you."

# Load spaCy's English model
model = spacy.load('en_core_web_sm')

# Display the dependency parse
tv.dependency_parse_visualizer_text(document, model=model, style='dep', jupyter=True)
dependency_parsing_visualization1 
document = "Luke Skywalker and Darth Vader are iconic characters in the Star Wars universe."

# Show the named entities
tv.dependency_parse_visualizer_text(document, model=model, style='ent', jupyter=True)
dependency_parsing_visualization1