title: How to solve Spanish lemmatization problems with SpaCy?
body: <p>When trying <strong>lemmatize in Spanish</strong> a csv with more than 60,000 words, <strong>SpaCy</strong> does not correctly write certain words, I understand that the model is not 100% accurate. However, I have not found any other solution, since <strong>NLTK</strong> does not bring a Spanish core.</p>
  
  <p>A friend tried to ask this question in Spanish Stackoverflow, however, the community is quite small compared with this community, and we got no answers about it.</p>
  
  <p><strong>code:</strong></p>
  
  <pre class="lang-py prettyprint-override"><code>nlp = spacy.load('es_core_news_sm')
  
  def lemmatizer(text):  
    doc = nlp(text)
    return ' '.join([word.lemma_ for word in doc])
  
  df['column'] = df['column'].apply(lambda x: lemmatizer(x))
  </code></pre>
  
  <p>I tried to lemmatize certain words that I found wrong to prove that SpaCy is not doing it correctly:</p>
  
  <pre class="lang-py prettyprint-override"><code>text = 'personas, ideas, cosas' 
  # translation: persons, ideas, things
  
  print(lemmatizer(text))
  </code></pre>
  
  <pre><code># Current output:
  personar , ideo , coser 
  # translation:
  personify, ideo, sew
  
  # The expected output should be:
  persona, idea, cosa
  
  # translation: 
  person, idea, thing
  </code></pre>