Fix typos in doc2vec-wikipedia notebook (piskvorky#1727)

VaiyeBe · Nov 26, 2017 · 6328f66 · 6328f66
1 parent b575955
commit 6328f66
Showing 1 changed file with 4 additions and 4 deletions.
diff --git a/docs/notebooks/doc2vec-wikipedia.ipynb b/docs/notebooks/doc2vec-wikipedia.ipynb
@@ -145,7 +145,7 @@
    },
    "source": [
     "## Preprocessing\n",
-    "To set the same vocabulary size with original papar. We first calculate the optimal **min_count** parameter."
+    "To set the same vocabulary size with original paper. We first calculate the optimal **min_count** parameter."
    ]
   },
   {
@@ -323,7 +323,7 @@
     "editable": true
    },
    "source": [
-    "After that, let's test both models! DBOW model show the simillar results with the original paper. First, calculating cosine simillarity of \"Machine learning\" using Paragraph Vector. Word Vector and Document Vector are separately stored. We have to add .docvecs after model name to extract Document Vector from Doc2Vec Model."
+    "After that, let's test both models! DBOW model show similar results with the original paper. First, calculating cosine similarity of \"Machine learning\" using Paragraph Vector. Word Vector and Document Vector are separately stored. We have to add .docvecs after model name to extract Document Vector from Doc2Vec Model."
    ]
   },
   {
@@ -513,9 +513,9 @@
     "editable": true
    },
    "source": [
-    "As a result, DBOW model demonstrate the similar artists with Lady Gaga in Japan such as 'Perfume', which is the Most famous Idol in Japan. On the other hand, DM model results don't include the Japanese aritsts in top 10 simillar documents. It's almost same with no vector calculated results.\n",
+    "As a result, DBOW model demonstrate similar artists to Lady Gaga in Japan such as 'Perfume', who is the most famous idol in Japan. On the other hand, DM model results don't include Japanese artists in top 10 similar documents. It's almost the same with no vector calculated results.\n",
     "\n",
-    "This results demonstrate that DBOW employed in the original paper is outstanding for calculating the similarity between Document Vector and Word Vector."
+    "These results demonstrate that the DBOW employed in the original paper is outstanding for calculating similarity between Document Vector and Word Vector."
    ]
   }
  ],