diff --git a/nlp/4_5_Semantic_Models.ipynb b/nlp/4_5_Semantic_Models.ipynb
index b2e9b9a..1a8851e 100644
--- a/nlp/4_5_Semantic_Models.ipynb
+++ b/nlp/4_5_Semantic_Models.ipynb
@@ -51,7 +51,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this session we provide a quick overview of the semantic models presented during the classes. In this case, we will use a real corpus so that we can extract meaningful patterns.\n",
+    "In this session, we provide a quick overview of the semantic models presented during the classes. In this case, we will use a real corpus so that we can extract meaningful patterns.\n",
     "\n",
     "The main objectives of this session are:\n",
     "* Understand the models and their differences\n",
@@ -69,9 +69,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are going to use on of the corpus that come prepackaged with Scikit-learn: the [20 newsgroup datase](http://qwone.com/~jason/20Newsgroups/). The 20  newsgroup dataset contains 20k documents that belong to 20 topics.\n",
+    "We are going to use one of the corpora that come prepackaged with Scikit-learn: the [20 newsgroup dataset](http://qwone.com/~jason/20Newsgroups/). The 20  newsgroup dataset contains 20k documents that belong to 20 topics.\n",
     "\n",
-    "We inspect now the corpus using the facilities from Scikit-learn, as explain in [scikit-learn](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html#newsgroups)"
+    "We inspect now the corpus using the facilities from Scikit-learn, as explained in [scikit-learn](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html#newsgroups)"
    ]
   },
   {
@@ -117,19 +117,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Converting Scikit-learn to gensim"
+    "# Converting Scikit-learn to gensim."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Although scikit-learn provides an LDA implementation, it is more popular the package *gensim*, which also provides an LSI implementation, as well as other functionalities. Fortunately, scikit-learn sparse matrices can be used in Gensim using the function *matutils.Sparse2Corpus()*. Anyway, if you are using intensively LDA,it can be convenient to create the corpus with their functions.\n",
+    "Although scikit-learn provides an LDA implementation, it is more popular than the package *gensim*, which also provides an LSI implementation, as well as other functionalities. Fortunately, scikit-learn sparse matrices can be used in Gensim using the function *matutils.Sparse2Corpus()*. Anyway, if you are using intensively LDA,it can be convenient to create the corpus with their functions.\n",
     "\n",
     "You should install first:\n",
     "\n",
     "* *gensim*. Run 'conda install gensim' in a terminal.\n",
-    "* *python-Levenshtein*. Run 'conda install python-Levenshtein' in a terminal"
+    "* *python-Levenshtein*. Run 'conda install python-Levenshtein' in a terminal."
    ]
   },
   {
@@ -183,7 +183,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Although scikit-learn provides an LDA implementation, it is more popular the package *gensim*, which also provides an LSI implementation, as well as other functionalities. Fortunately, scikit-learn sparse matrices can be used in Gensim using the function *matutils.Sparse2Corpus()*."
+    "Although scikit-learn provides an LDA implementation, it is more popular than the package *gensim*, which also provides an LSI implementation, as well as other functionalities. Fortunately, scikit-learn sparse matrices can be used in Gensim using the function *matutils.Sparse2Corpus()*."
    ]
   },
   {