Merge pull request #6 from recognai/chore/bump_nltk

Bump nltk version

README.md

@@ -28,16 +28,21 @@ pip install spacy-wordnet
 ````
 
 
+### Supported 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
+
 ````python
 
 import spacy
 
 from spacy_wordnet.wordnet_annotator import WordnetAnnotator 
 
-# Load an spacy model (supported models are "es" and "en") 
+# Load an spacy model (supported models are "es", "en" and "pt") 
 nlp = spacy.load('en')
 nlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger')
 token = nlp('prices')[0]
@@ -75,3 +80,36 @@ print(' '.join(enriched_sentence))
 
 ````
 
+### Portuguese example
+
+```python
+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
+```
+
+

requirements.txt

@@ -15,5 +15,5 @@
 # numpy==1.13.3
 # scipy==1.0
 
-nltk>=3.3,<3.4
+nltk>=3.4.5,<3.6
 spacy>=2.0,<3.0