Update 4_5_Semantic_Models.ipynb

Minor typos.

nlp/4_3_Vector_Representation.ipynb

@@ -239,7 +239,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/cif/anaconda3/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
+      "--",
       "  warnings.warn(msg, category=FutureWarning)\n"
      ]
     },
@@ -331,7 +331,7 @@
    "source": [
     "vectorizer = CountVectorizer(analyzer=\"word\", stop_words='english', binary=True) \n",
     "vectors = vectorizer.fit_transform(documents)\n",
-    "vectorizer.get_feature_names()"
+    "vectorizer.get_feature_names_out()"
    ]
   },
   {
@@ -363,9 +363,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vectorizer = CountVectorizer(analyzer=\"word\", stop_words='english', ngram_range=[2,2]) \n",
+    "vectorizer = CountVectorizer(analyzer=\"word\", stop_words='english', ngram_range=(2,2)) \n",
     "vectors = vectorizer.fit_transform(documents)\n",
-    "vectorizer.get_feature_names()"
+    "vectorizer.get_feature_names_out()"
    ]
   },
   {
@@ -401,7 +401,7 @@
     "\n",
     "vectorizer = TfidfVectorizer(analyzer=\"word\", stop_words='english')\n",
     "vectors = vectorizer.fit_transform(documents)\n",
-    "vectorizer.get_feature_names()"
+    "vectorizer.get_feature_names_out()"
    ]
   },
   {
@@ -429,9 +429,9 @@
     "train = [doc1, doc2, doc3]\n",
     "vectorizer = TfidfVectorizer(analyzer=\"word\", stop_words='english')\n",
     "\n",
-    "# We learn the vocabulary (fit) and tranform the docs into vectors\n",
+    "# We learn the vocabulary (fit) and transform the docs into vectors\n",
     "vectors = vectorizer.fit_transform(train)\n",
-    "vectorizer.get_feature_names()"
+    "vectorizer.get_feature_names_out()"
    ]
   },
   {

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()*."
    ]
   },
   {