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
withdrawin 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
You also need to install the following NLTK wordnet data:
python -m nltk.downloader wordnet
python -m nltk.downloader omwInstall
pip install spacy-wordnetSupported languages
We currently support Spanish, English and Portuguese, but we welcome contributions in order to add and test new languages supported by spaCy and NLTK.
Usage
English example
import spacy
from spacy_wordnet.wordnet_annotator import WordnetAnnotator
# Load an spacy model (supported models are "es", "en" and "pt")
nlp = spacy.load('en')
# Spacy 3.x
nlp.add_pipe("spacy_wordnet", after='tagger', config={'lang': nlp.lang})
# Spacy 2.x
# self.nlp_en.add_pipe(WordnetAnnotator(self.nlp_en.lang))
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:
lemmas_for_synset = [lemma for s in synsets for lemma in s.lemma_names()]
# 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:
lemmas_for_synset = [lemma for s in synsets for lemma in s.lemma_names('por')]
# 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
