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spaCy WordNet

spaCy Wordnet is a simple custom component for using WordNet, MultiWordnet and WordNet domains with spaCy.

The component combines the NLTK wordnet interface with WordNet domains to allow users to:

  • Get all synsets for a processed token. For example, getting all the synsets (word senses) of the word bank.
  • Get and filter synsets by domain. For example, getting synonyms of the verb withdraw in the financial domain.

Getting started

The spaCy WordNet component can be easily integrated into spaCy pipelines. You just need the following:

Prerequisites

  • Python 3.X
  • spaCy 2.X

You also need to install the following NLTK wordnet data:

python -m nltk.downloader wordnet
python -m nltk.downloader omw

Install

pip install spacy-wordnet

Supported languages

Almost all Open Multi Wordnet languages are supported.

Usage

English example

import spacy

from spacy_wordnet.wordnet_annotator import WordnetAnnotator 

# Load a spacy model (almost all Open Multi Wordnet languages are supported)
nlp = spacy.load('en')

# with spacy 2
nlp.add_pipe(WordnetAnnotator(nlp, 'wordnet'), after='tagger')
# with spacy 3
nlp.add_pipe('wordnet')
token = nlp('prices')[0]

# wordnet object link spacy token with nltk wordnet interface by giving acces to
# synsets and lemmas 
token._.wordnet.synsets()
token._.wordnet.lemmas()

# And automatically tags with wordnet domains
token._.wordnet.wordnet_domains()

spaCy WordNet lets you find synonyms by domain of interest for example economy

economy_domains = ['finance', 'banking']
enriched_sentence = []
sentence = nlp('I want to withdraw 5,000 euros')

# For each token in the sentence
for token in sentence:
    # We get those synsets within the desired domains
    synsets = token._.wordnet.wordnet_synsets_for_domain(economy_domains)
    if not synsets:
        enriched_sentence.append(token.text)
    else:
	lang = token._.wordnet.lang()
        lemmas_for_synset = [lemma for s in synsets for lemma in s.lemma_names(lang)]
        # If we found a synset in the economy domains
        # we get the variants and add them to the enriched sentence
        enriched_sentence.append('({})'.format('|'.join(set(lemmas_for_synset))))

# Let's see our enriched sentence
print(' '.join(enriched_sentence))
# >> I (need|want|require) to (draw|withdraw|draw_off|take_out) 5,000 euros

Portuguese example

import spacy

from spacy_wordnet.wordnet_annotator import WordnetAnnotator 

# Load an spacy model (you need to download the spacy pt model) 
nlp = spacy.load('pt')
nlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger')
text = "Eu quero retirar 5.000 euros"
economy_domains = ['finance', 'banking']
enriched_sentence = []
sentence = nlp(text)

# For each token in the sentence
for token in sentence:
    # We get those synsets within the desired domains
    synsets = token._.wordnet.wordnet_synsets_for_domain(economy_domains)
    if not synsets:
        enriched_sentence.append(token.text)
    else:
	lang = token._.wordnet.lang()
        lemmas_for_synset = [lemma for s in synsets for lemma in s.lemma_names(lang)]
        # If we found a synset in the economy domains
        # we get the variants and add them to the enriched sentence
        enriched_sentence.append('({})'.format('|'.join(set(lemmas_for_synset))))

# Let's see our enriched sentence
print(' '.join(enriched_sentence))
# >> Eu (querer|desejar|esperar) retirar 5.000 euros