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CUI-based Tree Visualizer for Universal Dependencies and Immediate Catena Analysis

Project description

Current PyPI packages

deplacy

Simple dependency visualizer for spaCy, UniDic2UD, Stanza, NLP-Cube, etc.

Usage with spaCy

>>> import spacy
>>> nlp=spacy.load("en_core_web_sm")
>>> doc=nlp("I saw a horse yesterday which had no name.")
>>> import deplacy
>>> deplacy.render(doc)
I         PRON  <══════════════╗   nsubj
saw       VERB  ═══════════╗═╗═╝═╗ ROOT
a         DET   <════════╗      det
horse     NOUN  ═══════╗═╝<     dobj
yesterday NOUN  <══════║═════╝    npadvmod
which     DET   <════╗           nsubj
had       AUX   ═══╗═╝<          relcl
no        DET   <               det
name      NOUN  ═╝<              dobj
.         PUNCT <════════════════╝ punct

deplacy.render(doc,BoxDrawingWidth=1,EnableCR=False,CatenaAnalysis=True,file=None) renders doc on a terminal. For old terminals, whose Box Drawing characters are "fullwidth", BoxDrawingWidth=2 nicely works. For several languages with "proportional" characters, EnableCR=True may work well. CatenaAnalysis=False disables Immediate Catena Analysis.

deplacy.serve(doc,port=5000) invokes a simple web-server to visualize doc with SVG. Try to connect http://127.0.0.1:5000 with your local browser. For Google Colaboratory, port=None visualizes doc directly on the notebook.

deplacy.dot(doc) returns raw DOT string for graphviz.Source.

Other usages

Visualization examples

deplacy.render()

deplacy.render()

deplacy.serve()

deplacy.serve()

deplacy.dot()

deplacy.dot()

Installation

pip install deplacy

You need to install spaCy, UniDic2UD, Stanza, or NLP-Cube separately. For Google Colaboratories, please follow (and edit) the templates shown below.

Templates for Google Colaboratory

Author

Koichi Yasuoka (安岡孝一)

Reference

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