diff --git a/nlp/4_5_Semantic_Models.ipynb b/nlp/4_5_Semantic_Models.ipynb
index e89e2dc..cef544f 100644
--- a/nlp/4_5_Semantic_Models.ipynb
+++ b/nlp/4_5_Semantic_Models.ipynb
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -85,7 +85,7 @@
        "(2034, 2807)"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -126,12 +126,15 @@
    "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",
     "\n",
-    "You should install first *gensim*. Run 'conda install -c anaconda gensim=0.12.4' in a terminal."
+    "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"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,23 +176,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[(0,\n",
-       "  '0.007*\"car\" + 0.006*\"increased\" + 0.006*\"closely\" + 0.006*\"groups\" + 0.006*\"center\" + 0.006*\"88\" + 0.006*\"offer\" + 0.005*\"archie\" + 0.005*\"beginning\" + 0.005*\"comets\"'),\n",
+       "  '0.011*\"baptist\" + 0.010*\"koresh\" + 0.009*\"bible\" + 0.006*\"reality\" + 0.006*\"virtual\" + 0.005*\"scarlet\" + 0.005*\"shag\" + 0.004*\"tootsie\" + 0.004*\"kinda\" + 0.004*\"captain\"'),\n",
        " (1,\n",
-       "  '0.005*\"allow\" + 0.005*\"discuss\" + 0.005*\"condition\" + 0.004*\"certain\" + 0.004*\"member\" + 0.004*\"manipulation\" + 0.004*\"little\" + 0.003*\"proposal\" + 0.003*\"heavily\" + 0.003*\"obvious\"'),\n",
+       "  '0.010*\"targa\" + 0.008*\"thanks\" + 0.008*\"moon\" + 0.007*\"craig\" + 0.007*\"zoroastrians\" + 0.006*\"yayayay\" + 0.005*\"unfortunately\" + 0.005*\"windows\" + 0.005*\"rayshade\" + 0.004*\"tdb\"'),\n",
        " (2,\n",
-       "  '0.002*\"led\" + 0.002*\"mechanism\" + 0.002*\"frank\" + 0.002*\"platform\" + 0.002*\"mormons\" + 0.002*\"concepts\" + 0.002*\"proton\" + 0.002*\"aeronautics\" + 0.002*\"header\" + 0.002*\"foreign\"'),\n",
+       "  '0.009*\"mary\" + 0.007*\"whatever\" + 0.006*\"god\" + 0.005*\"ns\" + 0.005*\"lucky\" + 0.005*\"joseph\" + 0.005*\"ssrt\" + 0.005*\"samaritan\" + 0.005*\"crusades\" + 0.004*\"phobos\"'),\n",
        " (3,\n",
-       "  '0.004*\"objects\" + 0.003*\"activity\" + 0.003*\"manhattan\" + 0.003*\"obtained\" + 0.003*\"eyes\" + 0.003*\"education\" + 0.003*\"netters\" + 0.003*\"complex\" + 0.003*\"europe\" + 0.002*\"missions\"')]"
+       "  '0.009*\"islam\" + 0.008*\"western\" + 0.008*\"plane\" + 0.008*\"jeff\" + 0.007*\"cheers\" + 0.007*\"kent\" + 0.007*\"joy\" + 0.007*\"khomeini\" + 0.007*\"davidian\" + 0.006*\"basically\"')]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -208,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -253,14 +256,14 @@
    ],
    "source": [
     "# You can save the dictionary\n",
-    "dictionary.save('newsgroup.dict')\n",
+    "dictionary.save('newsgroup.dict.texts')\n",
     "\n",
     "print(dictionary)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,28 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:random_state not set so using default value\n",
-      "WARNING:root:failed to load state from newsgroups.dict.state: [Errno 2] No such file or directory: 'newsgroups.dict.state'\n"
-     ]
-    }
-   ],
-   "source": [
-    "# You can optionally save the  dictionary \n",
-    "\n",
-    "dictionary.save('newsgroups.dict')\n",
-    "lda = LdaModel.load('newsgroups.dict')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,23 +359,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[(0,\n",
-       "  '0.011*\"thanks\" + 0.010*\"targa\" + 0.008*\"mary\" + 0.008*\"western\" + 0.007*\"craig\" + 0.007*\"jeff\" + 0.006*\"yayayay\" + 0.006*\"phobos\" + 0.005*\"unfortunately\" + 0.005*\"martian\"'),\n",
+       "  '0.009*\"whatever\" + 0.007*\"plane\" + 0.007*\"ns\" + 0.007*\"joy\" + 0.006*\"happy\" + 0.005*\"bob\" + 0.004*\"phil\" + 0.004*\"nasa\" + 0.003*\"purdue\" + 0.003*\"neie\"'),\n",
        " (1,\n",
-       "  '0.007*\"islam\" + 0.006*\"koresh\" + 0.006*\"moon\" + 0.006*\"bible\" + 0.006*\"plane\" + 0.006*\"ns\" + 0.005*\"zoroastrians\" + 0.005*\"joy\" + 0.005*\"lucky\" + 0.005*\"ssrt\"'),\n",
+       "  '0.009*\"god\" + 0.008*\"mary\" + 0.008*\"targa\" + 0.007*\"baptist\" + 0.007*\"thanks\" + 0.007*\"koresh\" + 0.006*\"really\" + 0.006*\"bible\" + 0.005*\"lot\" + 0.005*\"lucky\"'),\n",
        " (2,\n",
-       "  '0.009*\"whatever\" + 0.009*\"baptist\" + 0.007*\"cheers\" + 0.007*\"kent\" + 0.006*\"khomeini\" + 0.006*\"davidian\" + 0.005*\"gerald\" + 0.005*\"bull\" + 0.005*\"sorry\" + 0.005*\"jesus\"'),\n",
+       "  '0.010*\"moon\" + 0.007*\"phobos\" + 0.006*\"unfortunately\" + 0.006*\"martian\" + 0.006*\"russian\" + 0.005*\"rayshade\" + 0.005*\"anybody\" + 0.005*\"perturbations\" + 0.005*\"thanks\" + 0.004*\"apollo\"'),\n",
        " (3,\n",
-       "  '0.005*\"pd\" + 0.004*\"baltimore\" + 0.004*\"also\" + 0.003*\"ipx\" + 0.003*\"dam\" + 0.003*\"feiner\" + 0.003*\"foley\" + 0.003*\"ideally\" + 0.003*\"srgp\" + 0.003*\"thank\"')]"
+       "  '0.008*\"islam\" + 0.008*\"western\" + 0.007*\"jeff\" + 0.007*\"zoroastrians\" + 0.006*\"davidian\" + 0.006*\"basically\" + 0.005*\"bull\" + 0.005*\"gerald\" + 0.005*\"sorry\" + 0.004*\"kent\"')]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,14 +387,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0, 0.09401487), (1, 0.08991001), (2, 0.08514047), (3, 0.7309346)]\n"
+      "[(0, 0.7154438), (1, 0.10569019), (2, 0.09522807), (3, 0.08363795)]\n"
      ]
     }
    ],
@@ -424,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -445,14 +427,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0, 0.06678458), (1, 0.8006135), (2, 0.06974816), (3, 0.062853776)]\n"
+      "[(0, 0.06320839), (1, 0.80878526), (2, 0.06274223), (3, 0.065264106)]\n"
      ]
     }
    ],
@@ -464,14 +446,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.007*\"islam\" + 0.006*\"koresh\" + 0.006*\"moon\" + 0.006*\"bible\" + 0.006*\"plane\" + 0.006*\"ns\" + 0.005*\"zoroastrians\" + 0.005*\"joy\" + 0.005*\"lucky\" + 0.005*\"ssrt\"\n"
+      "0.009*\"god\" + 0.008*\"mary\" + 0.008*\"targa\" + 0.007*\"baptist\" + 0.007*\"thanks\" + 0.007*\"koresh\" + 0.006*\"really\" + 0.006*\"bible\" + 0.005*\"lot\" + 0.005*\"lucky\"\n"
      ]
     }
    ],
@@ -482,15 +464,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[(0, 0.110989906), (1, 0.670005), (2, 0.11422917), (3, 0.10477593)]\n",
-      "0.007*\"islam\" + 0.006*\"koresh\" + 0.006*\"moon\" + 0.006*\"bible\" + 0.006*\"plane\" + 0.006*\"ns\" + 0.005*\"zoroastrians\" + 0.005*\"joy\" + 0.005*\"lucky\" + 0.005*\"ssrt\"\n"
+      "[(0, 0.10564032), (1, 0.67894983), (2, 0.104482815), (3, 0.11092702)]\n",
+      "0.009*\"god\" + 0.008*\"mary\" + 0.008*\"targa\" + 0.007*\"baptist\" + 0.007*\"thanks\" + 0.007*\"koresh\" + 0.006*\"really\" + 0.006*\"bible\" + 0.005*\"lot\" + 0.005*\"lucky\"\n"
      ]
     }
    ],
@@ -510,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,23 +508,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[(0,\n",
-       "  '0.769*\"god\" + 0.345*\"jesus\" + 0.235*\"bible\" + 0.203*\"christian\" + 0.149*\"christians\" + 0.108*\"christ\" + 0.089*\"well\" + 0.085*\"koresh\" + 0.081*\"kent\" + 0.080*\"christianity\"'),\n",
+       "  '0.769*\"god\" + 0.346*\"jesus\" + 0.235*\"bible\" + 0.204*\"christian\" + 0.148*\"christians\" + 0.107*\"christ\" + 0.090*\"well\" + 0.085*\"koresh\" + 0.081*\"kent\" + 0.080*\"christianity\"'),\n",
        " (1,\n",
-       "  '-0.863*\"thanks\" + -0.255*\"please\" + -0.160*\"hello\" + -0.153*\"hi\" + 0.123*\"god\" + -0.112*\"sorry\" + -0.088*\"could\" + -0.075*\"windows\" + -0.068*\"jpeg\" + -0.062*\"gif\"'),\n",
+       "  '-0.863*\"thanks\" + -0.255*\"please\" + -0.159*\"hello\" + -0.152*\"hi\" + 0.124*\"god\" + -0.111*\"sorry\" + -0.088*\"could\" + -0.074*\"windows\" + -0.067*\"jpeg\" + -0.063*\"gif\"'),\n",
        " (2,\n",
-       "  '-0.779*\"well\" + 0.229*\"god\" + -0.164*\"yes\" + 0.153*\"thanks\" + -0.135*\"ico\" + -0.135*\"tek\" + -0.132*\"beauchaine\" + -0.132*\"queens\" + -0.132*\"bronx\" + -0.131*\"manhattan\"'),\n",
+       "  '-0.780*\"well\" + 0.229*\"god\" + -0.165*\"yes\" + 0.154*\"thanks\" + -0.133*\"ico\" + -0.133*\"tek\" + -0.130*\"queens\" + -0.130*\"bronx\" + -0.130*\"beauchaine\" + -0.130*\"manhattan\"'),\n",
        " (3,\n",
-       "  '0.343*\"well\" + -0.335*\"ico\" + -0.334*\"tek\" + -0.328*\"bronx\" + -0.328*\"beauchaine\" + -0.328*\"queens\" + -0.325*\"manhattan\" + -0.305*\"com\" + -0.303*\"bob\" + -0.073*\"god\"')]"
+       "  '-0.338*\"well\" + 0.336*\"ico\" + 0.334*\"tek\" + 0.328*\"bronx\" + 0.328*\"beauchaine\" + 0.328*\"queens\" + 0.326*\"manhattan\" + 0.305*\"com\" + 0.305*\"bob\" + 0.072*\"god\"')]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -603,6 +585,15 @@
   }
  ],
  "metadata": {
+  "datacleaner": {
+   "position": {
+    "top": "50px"
+   },
+   "python": {
+    "varRefreshCmd": "try:\n    print(_datacleaner.dataframe_metadata())\nexcept:\n    print([])"
+   },
+   "window_display": false
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -618,7 +609,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,