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        "author": "Francisco Aranda",
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        "description": "# spaCy WordNet\n\nspaCy Wordnet is a simple custom component for using [WordNet](https://wordnet.princeton.edu/), [MultiWordnet](http://multiwordnet.fbk.eu/english/home.php) and [WordNet domains](http://wndomains.fbk.eu/) with [spaCy](http://spacy.io).\n\nThe component combines the [NLTK wordnet interface](http://www.nltk.org/howto/wordnet.html) with WordNet domains to allow users to:\n\n* Get all synsets for a processed token. For example, getting all the synsets (word senses) of the word ``bank``.\n* Get and filter synsets by domain. For example, getting synonyms of the verb ``withdraw`` in the financial domain.\n\n \n## Getting started\nThe spaCy WordNet component can be easily integrated into spaCy pipelines. You just need the following:\n### Prerequisites\n\n* Python 3.X\n* spaCy\n\nYou also need to install the following NLTK wordnet data:\n\n````bash\npython -m nltk.downloader wordnet\npython -m nltk.downloader omw\n````\n### Install\n\n````bash\npip install spacy-wordnet\n````\n\n\n\n## Usage\n\n````python\n\nimport spacy\n\nfrom spacy_wordnet.wordnet_annotator import WordnetAnnotator \n\n# Load an spacy model (supported models are \"es\" and \"en\") \nnlp = spacy.load('en')\nnlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger')\ntoken = nlp('prices')[0]\n\n# wordnet object link spacy token with nltk wordnet interface by giving acces to\n# synsets and lemmas \ntoken._.wordnet.synsets()\ntoken._.wordnet.lemmas()\n\n# And automatically tags with wordnet domains\ntoken._.wordnet.wordnet_domains()\n\n# Imagine we want to enrich the following sentence with synonyms\nsentence = nlp('I want to withdraw 5,000 euros')\n\n# spaCy WordNet lets you find synonyms by domain of interest\n# for example economy\neconomy_domains = ['finance', 'banking']\nenriched_sentence = []\n\n# For each token in the sentence\nfor token in sentence:\n    # We get those synsets within the desired domains\n    synsets = token._.wordnet.wordnet_synsets_for_domain(economy_domains)\n    if synsets:\n        lemmas_for_synset = []\n        for s in synsets:\n            # If we found a synset in the economy domains\n            # we get the variants and add them to the enriched sentence\n            lemmas_for_synset.extend(s.lemma_names())\n            enriched_sentence.append('({})'.format('|'.join(set(lemmas_for_synset))))\n    else:\n        enriched_sentence.append(token.text)\n\n# Let's see our enriched sentence\nprint(' '.join(enriched_sentence))\n# >> I (need|want|require) to (draw|withdraw|draw_off|take_out) 5,000 euros\n    \n````",
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