diff --git a/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb
index ffa4e533..e11b248b 100644
--- a/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb
+++ b/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb
@@ -432,7 +432,7 @@
         "\n",
         "**FastText** tries to overcome the second limitation, and builds on Word2Vec by learning vector representations for each word and the charachter n-grams found within each word. The values of the representations are then averaged into one vector at each training step. While this adds a lot of additional computation to pretraining, it enables word embeddings to encode sub-word information.\n",
         "\n",
-        "Another method, **GloVe**, uses a different approach to word embeddings, based on the factorization of the word-context matrix. First, it builds a large matrix that counts the number of word occurences in different contexts, and then it tries to represent this matrix in lower dimensions in a way that minimizes reconstruction loss.\n",
+        "Another method, **GloVe**, uses a different approach to word embeddings, based on the factorization of the word-context matrix. First, it builds a large matrix that counts the number of word occurrences in different contexts, and then it tries to represent this matrix in lower dimensions in a way that minimizes reconstruction loss.\n",
         "\n",
         "The gensim library supports those word embeddings, and you can experiment with them by changing the model loading code above."
       ]