diff --git a/embeddings.ipynb b/embeddings.ipynb
index 151904b5b913efb6d2b86b48e8a329f0707d8039..62b8b931527d51e18a7c6c9b86525ab80ebc1956 100644
--- a/embeddings.ipynb
+++ b/embeddings.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,70 +23,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['austen-emma.txt',\n",
-       " 'austen-persuasion.txt',\n",
-       " 'austen-sense.txt',\n",
-       " 'bible-kjv.txt',\n",
-       " 'blake-poems.txt',\n",
-       " 'bryant-stories.txt',\n",
-       " 'burgess-busterbrown.txt',\n",
-       " 'carroll-alice.txt',\n",
-       " 'chesterton-ball.txt',\n",
-       " 'chesterton-brown.txt',\n",
-       " 'chesterton-thursday.txt',\n",
-       " 'edgeworth-parents.txt',\n",
-       " 'melville-moby_dick.txt',\n",
-       " 'milton-paradise.txt',\n",
-       " 'shakespeare-caesar.txt',\n",
-       " 'shakespeare-hamlet.txt',\n",
-       " 'shakespeare-macbeth.txt',\n",
-       " 'whitman-leaves.txt']"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# nltk.download('brown')\n",
-    "gutenberg.fileids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "lemmatizer = WordNetLemmatizer()\n",
     "\n",
     "def filter(token):\n",
+    "    \"\"\"Return if token should be ignored or not.\n",
+    "    \"\"\"\n",
+    "    # do not keep tokens which include digits or punctuation\n",
     "    result = not re.search(\"[{}\\d]\".format(string.punctuation), token)\n",
-    "#     result &= len(wn.synsets(token)) != 0\n",
     "    return result\n",
     "\n",
     "def replace_token(token):\n",
+    "    \"\"\"Replace token with its lemma.\n",
+    "    \"\"\"\n",
+    "    token = token.lower()\n",
+    "    # lemmatize token\n",
     "    token = lemmatizer.lemmatize(token, \"v\")\n",
     "    token = lemmatizer.lemmatize(token)\n",
     "    return token\n",
     "\n",
     "def get_sentences(sentences):\n",
-    "    sentences = [[replace_token(token.lower()) for token in sent] for sent in sentences]\n",
+    "    \"\"\"Return edited set of sentences.\n",
+    "    \n",
+    "    Args:\n",
+    "        sentences (List[List[str]]): list of sentences to prepare\n",
+    "    \"\"\"\n",
+    "    # first, filter out tokens\n",
     "    sentences = [[token for token in sent if filter(token)] for sent in sentences]\n",
+    "    # second, replace tokens with lowercase lemma\n",
+    "    sentences = [[replace_token(token) for token in sent] for sent in sentences]\n",
     "    return sentences\n",
     "\n",
     "def plot_clusters(clusters, index2vector, index2word, num_per_cluster = 10, ):\n",
-    "    # plot clusters without regard to similarity\n",
-    "\n",
-    "    # number of words to show per cluster\n",
-    "    num_per_cluster = 10\n",
+    "    \"\"\"Plot a list of clusters.\n",
+    "    \n",
+    "    This function plots random instances of embeddings from each cluster and tries to label\n",
+    "    each cluster with the least common wordnet subsumer of the plotted words.\n",
+    "    \n",
+    "    Args:\n",
+    "        clusters (List[List[int]]): the clusters to plot\n",
+    "        index2vector (ndarray): array which maps indices to their embeddings\n",
+    "        index2word (List[str]): list which maps indices to their words\n",
+    "        num_per_cluster (int): number of instances to plot for each cluster\n",
+    "    \"\"\"\n",
     "\n",
     "    plt.figure(figsize = (25,25))\n",
     "\n",
@@ -97,33 +79,42 @@
     "    for i, cluster in enumerate(clusters[:]):\n",
     "        # select color\n",
     "        color = colors[i]\n",
-    "        # choose random words to display\n",
+    "        # choose random word indices to display\n",
     "        random_instances = np.random.choice(cluster, min(len(cluster), num_per_cluster), replace = False)\n",
     "\n",
-    "        # list/iterator of words that exist in word net\n",
+    "        # iterator which only contains the selected words which are available in wordnet\n",
     "        wn_words = [word for word in index2word[random_instances] if len(wn.synsets(word)) != 0]\n",
     "        wn_words = iter(wn_words)\n",
     "        # the lowest common subsumer of the words in a cluster\n",
     "        common_hypernym = None\n",
     "        for word in wn_words:\n",
-    "            # if lcs is not set yet, find lcs of current word and next word in the iterator\n",
+    "            # find initial common hypernym of the first word and a succeeding word in the cluster\n",
     "            if common_hypernym is None:\n",
-    "                try:\n",
-    "                    common_hypernyms = wn.synsets(word)[0].lowest_common_hypernyms(wn.synsets(next(wn_words))[0])\n",
-    "                except:\n",
-    "                    break\n",
+    "                while common_hypernym is None:\n",
+    "                    # if there are still words left in the iterator, search for lcs \n",
+    "                    try:\n",
+    "                        common_hypernyms = wn.synsets(word)[0].lowest_common_hypernyms(wn.synsets(next(wn_words))[0])\n",
+    "                        # if at least 1 lcs was found, set the first one as the current lcs and jump out of the while loop\n",
+    "                        if len(common_hypernyms) != 0:\n",
+    "                            common_hypernym = common_hypernyms[0]\n",
+    "                            continue\n",
+    "                    # if there are no more words left, jump out of the loop\n",
+    "                    except:\n",
+    "                        break\n",
     "            # otherwise, find lcs of current lcs and current word\n",
     "            else:\n",
     "                common_hypernyms = wn.synsets(word)[0].lowest_common_hypernyms(common_hypernym)\n",
     "\n",
-    "            # if at least 1 lcs was found, set the first one as the current lcs\n",
-    "            if len(common_hypernyms) != 0:\n",
-    "                common_hypernym = common_hypernyms[0]\n",
+    "                # if at least 1 lcs was found, set the first one as the current lcs\n",
+    "                if len(common_hypernyms) != 0:\n",
+    "                    common_hypernym = common_hypernyms[0]\n",
     "\n",
     "        # display the randomly chosen words of the current cluster and create a legend for the cluster with the lcs as label\n",
     "        for i, (x,y) in enumerate(index2vector[random_instances]):\n",
+    "            # set the label for the first word in the cluster\n",
     "            if i == 0:\n",
     "                plt.scatter(x, y, color = color, label = str(common_hypernym))\n",
+    "            # plot the remaining words without a label\n",
     "            else:\n",
     "                plt.scatter(x, y, color = color)\n",
     "\n",
@@ -140,27 +131,23 @@
     "    plt.savefig(\"embeddings.png\")\n",
     "    plt.show()\n",
     "    \n",
-    "def get_kmeans_clusters(n_clusters, vectors):\n",
-    "    # obtain clusters with kmeans\n",
-    "    kmeans = KMeans(n_clusters = n_clusters).fit(vectors)\n",
-    "    clusters = [np.array([i for i, cluster in enumerate(kmeans.labels_) if cluster == j]) \n",
-    "                for j in range(min(kmeans.labels_), max(kmeans.labels_) + 1)]\n",
-    "    return clusters\n",
+    "def get_clusters(kind, vectors, n_clusters = None, min_samples = None, eps = None):\n",
+    "    if kind == \"kmeans\":\n",
+    "        assert n_clusters != None\n",
+    "        clusters = KMeans(n_clusters = n_clusters).fit(vectors)\n",
+    "    elif kind == \"dbscan\":\n",
+    "        assert min_samples != None and eps != None\n",
+    "        clusters = DBSCAN(eps = eps, min_samples = min_samples).fit(vectors)\n",
+    "    else:\n",
+    "        assert min_samples != None and eps != None\n",
+    "        clusters = OPTICS(min_samples = min_samples, max_eps = eps).fit(vectors)\n",
+    "        \n",
+    "    # create list of arrays which contain the indices of the corresponding cluster\n",
+    "    clusters = [np.array([i for i, cluster in enumerate(clusters.labels_) if cluster == j]) \n",
+    "                for j in range(min(clusters.labels_), max(clusters.labels_) + 1)]\n",
+    "    \n",
+    "    print(\"Number of found clusters: \", len(clusters))\n",
     "    \n",
-    "def get_dbscan_clusters(eps, min_samples, vectors):\n",
-    "    # obtain clusters with dbscan\n",
-    "    dbscan = DBSCAN(eps = eps, min_samples = min_samples).fit(vectors)\n",
-    "    clusters = [np.array([i for i, cluster in enumerate(dbscan.labels_) if cluster == j]) \n",
-    "                for j in range(min(dbscan.labels_), max(dbscan.labels_) + 1)]\n",
-    "    print(\"Number of dbscan clusters: \", len(clusters))\n",
-    "    return clusters\n",
-    "\n",
-    "def get_optics_clusters(min_samples, max_eps, vectors):\n",
-    "    # obtain clusters with optics\n",
-    "    optics = OPTICS(min_samples = min_samples, max_eps = max_eps).fit(vectors)\n",
-    "    clusters = [np.array([i for i, cluster in enumerate(optics.labels_) if cluster == j]) \n",
-    "                for j in range(min(optics.labels_), max(optics.labels_) + 1)]\n",
-    "    print(\"Number of optics clusters: \", len(clusters))\n",
     "    return clusters"
    ]
   },
@@ -191,76 +178,33 @@
    "outputs": [],
    "source": [
     "# train word2vec model on a set of sentences\n",
-    "word2vec = gensim.models.Word2Vec(sentences, size = 50, iter = 500, min_count = 10, window = 10, negative = 5)\n",
-    "word2vec.save(\"word2vec.model\")"
+    "word2vec = gensim.models.Word2Vec(sentences, size = 50, iter = 200, min_count = 10, window = 10, negative = 5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.08847566 0.07616815]\n",
-      "Number of optics clusters:  15\n"
-     ]
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1800x1800 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "index2word = np.array(word2vec.wv.index2word)[:100]\n",
-    "word2index = {word: index for index, word in enumerate(index2word)}\n",
-    "index2vector = np.array(word2vec.wv.vectors)[:100]\n",
-    "\n",
-    "pca = PCA(n_components = 2)\n",
-    "pca.fit(index2vector)\n",
-    "print(pca.explained_variance_ratio_)\n",
-    "index2vector = np.matmul(index2vector, np.transpose(pca.components_))\n",
-    "\n",
-    "clusters = get_optics_clusters(2, .5, index2vector)\n",
-    "\n",
-    "plot_clusters(clusters, index2vector, index2word)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of optics clusters:  21\n",
-      "[0.18160246 0.10724092]\n"
+      "Number of found clusters:  23\n",
+      "Explained variance:  [0.2385597  0.11066052]\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\robin\\anaconda3\\envs\\text-mining\\lib\\site-packages\\ipykernel_launcher.py:14: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+      "C:\\Users\\robin\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:14: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
       "  \n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1800x1800 with 1 Axes>"
       ]
@@ -283,86 +227,36 @@
     "index2vector = np.array(word2vec.wv.vectors)\n",
     "    \n",
     "# first, obtain clusters from the initital word2vec vectors\n",
-    "initial_clusters = get_optics_clusters(2, 40.0, index2vector)\n",
+    "initial_clusters = get_clusters(\"optics\", index2vector, min_samples = 2, eps = 40.0)\n",
     "# get the most frequent (smallest index) word from every cluster\n",
     "instance_per_cluster = [min(cluster) for cluster in np.random.choice(initial_clusters, 8, replace = False)]\n",
     "# for every most frequent word of a cluster, get the 10 most similar words of that word\n",
     "sim_clusters = [[index] + [word2index[word] \n",
     "                           for word, _ in word2vec.wv.most_similar(positive=[index2word[index]], topn = 5)]\n",
     "               for index in instance_per_cluster]\n",
-    "# list of indices\n",
+    "# put all indices in one list\n",
     "indices = [index for cluster in sim_clusters for index in cluster]\n",
     "\n",
     "index2word = index2word[indices]\n",
+    "# map the initial indices to the indices that were selected above\n",
     "old_index2new_index = {old_index: new_index for new_index, old_index in enumerate(indices)}\n",
     "word2index = {word: index for index, word in enumerate(index2word)}\n",
     "index2vector = index2vector[indices]\n",
     "\n",
-    "# fit a pca model on the randomly sampled instances\n",
+    "# fit a pca model on the selected indices\n",
+    "pca = PCA(n_components = 2)\n",
     "pca.fit(index2vector)\n",
     "# print explained variance ratios\n",
-    "print(pca.explained_variance_ratio_)\n",
+    "print(\"Explained variance: \", pca.explained_variance_ratio_)\n",
     "# obtain the new, 2 dimensional, word embeddings for the selected instances\n",
     "index2vector = np.matmul(index2vector, np.transpose(pca.components_))\n",
     "\n",
+    "# get the initial \"similarity clusters\" with the new indices\n",
     "clusters = [[old_index2new_index[index] for index in cluster] for cluster in sim_clusters]\n",
-    "# clusters = get_kmeans_clusters(20, index2vector)\n",
-    "\n",
     "\n",
     "# plot the clusters\n",
     "plot_clusters(clusters, index2vector, index2word, 10)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x1800 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# obtain clusters for the 2d vectors\n",
-    "# clusters = get_dbscan_clusters(0.9, 3, index2vector)\n",
-    "# clusters = get_kmeans_clusters(20, index2vector)\n",
-    "\n",
-    "clusters = [[old_index2new_index[index] for index in cluster] for cluster in sim_clusters]\n",
-    "\n",
-    "# plot the clusters\n",
-    "plot_clusters(clusters[:2], index2vector, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x1800 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_clusters(sim_clusters, clustered_index2vector)"
-   ]
   }
  ],
  "metadata": {