An Introduction to Word Embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An Introduction to Word Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many people would say the breakthrough of deep learning in Natural Language Processing started with the introduction of word embeddings. Rather than using the words themselves as features, neural network methods typically take as input dense, relatively low-dimensional vectors that model the meaning and usage of a word. Word embeddings were first popularized through the [Word2Vec](https://arxiv.org/abs/1301.3781) model, developed by Thomas Mikolov and colleagues at Google. Since then, scores of alternative approaches have been developed, such as [GloVe](https://nlp.stanford.edu/projects/glove/) and [FastText](https://fasttext.cc/) embeddings. In this notebook, we'll explore word embeddings with the original Word2Vec approach, as implemented in the [Gensim](https://radimrehurek.com/gensim/) library. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training word embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training word embeddings with Gensim couldn't be easier. The only thing we need is a corpus of sentences in the language under investigation. For our experiments, we're going to use the abstracts of all ArXiv papers in the category cs.CL (computation and language) that were published before mid-April 2021 — a total of around 25,000 documents. We tokenize these abstracts with spaCy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import spacy\n",
    "\n",
    "class Corpus(object):\n",
    "\n",
    "    def __init__(self, filename):\n",
    "        self.filename = filename\n",
    "        self.nlp = spacy.blank(\"en\")\n",
    "        \n",
    "    def __iter__(self):\n",
    "        with open(self.filename, \"r\") as i:\n",
    "            reader = csv.reader(i, delimiter=\",\")\n",
    "            for _, abstract in reader:\n",
    "                tokens = [t.text.lower() for t in self.nlp(abstract)]\n",
    "                yield tokens\n",
    "                            \n",
    "                    \n",
    "documents = Corpus(\"data/arxiv/arxiv.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we train our word embeddings, gensim allows us to set a number of parameters. The most important of these are `min_count`, `window`, `vector_size` and `sg`:\n",
    "\n",
    "- `min_count` is the minimum frequency of the words in our corpus. For infrequent words, we just don't have enough information to train reliable word embeddings. It therefore makes sense to set this minimum frequency to at least 10. In these experiments, we'll set it to 100 to limit the size of our model even more.\n",
    "- `window` is the number of words to the left and to the right that make up the context that word2vec will take into account.\n",
    "- `vector_size` is the dimensionality of the word vectors. This is generally between 100 and 1000. This dimensionality often forces us to make a trade-off: embeddings with a higher dimensionality are able to model more information, but also need more data to train.\n",
    "- `sg`: there are two algorithms to train word2vec: skip-gram and CBOW. Skip-gram tries to predict the context on the basis of the target word; CBOW tries to find the target on the basis of the context. By default, Gensim uses CBOW (`sg=0`).\n",
    "\n",
    "We'll investigate the impact of some of these parameters later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "\n",
    "model = gensim.models.Word2Vec(documents, min_count=100, window=5, vector_size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using word embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the trained model. The word embeddings are on its `wv` attribute, and we can access them by the using the token as key. For example, here is the embedding for *nlp*, with the requested 100 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.6964098 , -0.53644496,  1.6920139 , -0.73675466,  0.08380616,\n",
       "       -0.40894854,  0.84449   ,  2.7349243 , -1.4700398 ,  2.4422982 ,\n",
       "       -0.9052107 , -1.9871653 ,  1.4523842 ,  0.81846136,  0.88837194,\n",
       "        0.78548586, -1.3568974 ,  1.1884927 ,  0.76052207,  2.4873302 ,\n",
       "        1.0235857 , -0.46776208,  0.24462815,  0.8840933 , -1.8226203 ,\n",
       "       -1.258582  ,  0.71849775,  1.2371403 ,  1.0342939 , -0.7588491 ,\n",
       "        1.4424444 ,  0.18996137,  1.2168573 ,  1.2155888 ,  0.9064627 ,\n",
       "       -0.42327297, -0.2555004 , -4.6350546 ,  0.1400926 ,  2.5591917 ,\n",
       "       -1.8521487 , -1.6599368 ,  1.6274469 , -0.11764257, -2.2103846 ,\n",
       "        0.22001874,  2.1117709 ,  0.7829827 ,  0.5087813 , -0.49775454,\n",
       "        1.9418803 ,  1.6074972 , -0.98678124, -0.27739003,  0.42102492,\n",
       "        0.8500456 ,  1.066767  ,  1.6491311 ,  2.7145324 , -0.35392433,\n",
       "       -0.7773586 ,  2.4440873 , -0.98244476, -0.50990117,  0.37844443,\n",
       "       -1.2001766 , -0.9464699 , -2.7689419 ,  0.9285035 , -1.393094  ,\n",
       "       -2.6368618 , -1.9721911 ,  0.41437167,  1.171227  , -0.7060545 ,\n",
       "        1.2539046 ,  1.644871  ,  0.20853965, -1.914305  ,  1.2996354 ,\n",
       "        1.5589004 ,  2.8639042 ,  0.41040063, -0.76020986, -0.98392844,\n",
       "        2.281242  , -0.21957877, -1.6757928 , -1.1541871 ,  0.3213301 ,\n",
       "        0.47595787, -0.08929498, -0.96834105,  1.6429704 , -1.4209185 ,\n",
       "       -1.1482185 , -1.6995147 , -0.40935686, -0.47653005, -1.7113159 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv[\"nlp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also easily find the similarity between two words. Similarity is measured as the cosine between the two word embeddings, and therefore ranges between -1 and +1. The higher the cosine, the more similar two words are. As expected, the figures below show that *nmt* (neural machine translation) is closer to *smt* (statistical machine translation) than to *ner* (named entity recognition)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.647619\n",
      "0.4019784\n"
     ]
    }
   ],
   "source": [
    "print(model.wv.similarity(\"nmt\", \"smt\"))\n",
    "print(model.wv.similarity(\"nmt\", \"ner\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a similar vein, we can find the words that are most similar to a target word. The words with the most similar embedding to *bert* are all semantically related to it: other types of pretrained models such as *roberta*, *mbert*, *xlm*, as well as the more general model type BERT represents (*transformer* and *transformers*), and more generally related words (*pretrained*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('roberta', 0.7886296510696411),\n",
       " ('transformer', 0.7474736571311951),\n",
       " ('elmo', 0.7299053072929382),\n",
       " ('transformers', 0.6981489658355713),\n",
       " ('pretrained', 0.6802968978881836),\n",
       " ('xlnet', 0.6786019802093506),\n",
       " ('gpt-2', 0.6747310161590576),\n",
       " ('mbert', 0.6422373056411743),\n",
       " ('xlm', 0.641656219959259),\n",
       " ('lstm', 0.6408851146697998)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.similar_by_word(\"bert\", topn=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, we can look for words that are similar to a set of words and dissimilar to another set of words at the same time. This allows us to look for analogies of the type *BERT is to a transformer like an LSTM is to ...*. Our embedding model correctly predicts that LSTMs are a type of RNN, just like BERT is a particular type of transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('rnn', 0.7859204411506653)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(positive=[\"transformer\", \"lstm\"], negative=[\"bert\"], topn=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can also zoom in on one of the meanings of ambiguous words. For example, in NLP *tree* has a very specific meaning, which is obvious from its nearest neighbours *constituency*, *parse*, *dependency* and *syntax*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('trees', 0.7918293476104736),\n",
       " ('parse', 0.7395621538162231),\n",
       " ('recursive', 0.7018074989318848),\n",
       " ('constituency', 0.6915127038955688),\n",
       " ('dependency', 0.6613419055938721),\n",
       " ('syntax', 0.6261685490608215),\n",
       " ('constituent', 0.6240048408508301),\n",
       " ('kernels', 0.6202076077461243),\n",
       " ('graph', 0.5948659777641296),\n",
       " ('formalism', 0.59360671043396)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(positive=[\"tree\"], topn=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we specify we're looking for words that are similar to *tree*, but dissimilar to *syntax*, suddenly its standard meaning takes over, and *forest* crops up in its nearest neighbours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('forest', 0.4965582489967346),\n",
       " ('nearest', 0.47338199615478516),\n",
       " ('logistic', 0.46190744638442993),\n",
       " ('normalized', 0.45594778656959534),\n",
       " ('bayes', 0.4521142244338989),\n",
       " ('greedy', 0.4447275698184967),\n",
       " ('crf', 0.41870537400245667),\n",
       " ('stochastic', 0.4143545925617218),\n",
       " ('random', 0.4126051962375641),\n",
       " ('softmax', 0.4118269085884094)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(positive=[\"tree\"], negative=[\"syntax\"], topn=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can present the word2vec model with a list of words and ask it to identify the odd one out. It then uses the word embeddings to identify the word that is least similar to the other ones. For example, in the list *lstm cnn gru svm transformer*, it correctly identifies *svm* as the only non-neural model. In the list *bert word2vec gpt-2 roberta xlnet*, it correctly singles out *word2vec* as the only non-transormer model. In *word2vec bert glove fasttext elmo*, *bert* is singled out as the only transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm\n",
      "word2vec\n",
      "bert\n"
     ]
    }
   ],
   "source": [
    "print(model.wv.doesnt_match(\"lstm cnn gru svm transformer\".split()))\n",
    "print(model.wv.doesnt_match(\"bert word2vec gpt-2 roberta xlnet\".split()))\n",
    "print(model.wv.doesnt_match(\"word2vec bert glove fasttext elmo\".split()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now visualize some of our embeddings. To plot embeddings with a dimensionality of 100 or more, we first need to map them to a dimensionality of 2. We do this with the popular [t-SNE](https://lvdmaaten.github.io/tsne/) method. T-SNE, short for t-distributed Stochastic Neighbor Embedding, helps us visualize high-dimensional data by mapping similar data to nearby points and dissimilar data to distance points in the low-dimensional space.\n",
    "\n",
    "T-SNE is present in [Scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html). To run it, we just have to specify the number of dimensions we'd like to map the data to (`n_components`), and the similarity metric that t-SNE should use to compute the similarity between two data points (`metric`). We're going to map to 2 dimensions and use the cosine as our similarity metric. Additionally, we use PCA as an initialization method to remove some noise and speed up computation. The [Scikit-learn user guide](https://scikit-learn.org/stable/modules/manifold.html#t-sne) contains some additional tips for optimizing performance. \n",
    "\n",
    "Plotting all the embeddings in our vector space would result in a very crowded figure where the labels are hardly legible. Therefore we'll focus on a subset of embeddings by selecting the 200 most similar words to a target word. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "target_word = \"bert\"\n",
    "selected_words = [w[0] for w in model.wv.most_similar(positive=[target_word], topn=200)] + [target_word]\n",
    "embeddings = [model.wv[w] for w in selected_words] + model.wv[\"bert\"]\n",
    "\n",
    "mapped_embeddings = TSNE(n_components=2, metric='cosine', init='pca').fit_transform(embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we take *bert* as our target word, the figure shows some interesting patterns. In the immediate vicinity of *bert*, we find the similar transformer models that we already identified as its nearest neighbours earlier: *xlm*, *mbert*, *gpt-2*, and so on. Other parts of the picture have equally informative clusters of NLP tasks and benchmarks (*squad* and *glue*), languages (*german* and *english*), neural-network architectures (*lstm*, *gru*, etc.), embedding types (*word2vec*, *glove*, *fasttext*, *elmo*), etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "x = mapped_embeddings[:,0]\n",
    "y = mapped_embeddings[:,1]\n",
    "plt.scatter(x, y)\n",
    "\n",
    "for i, txt in enumerate(selected_words):\n",
    "    plt.annotate(txt, (x[i], y[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We mentioned above there are a number of parameters we can set when training our embeddings. Let's investigate the impact some of these have on the result. Quantifying the quality of embeddings is a hard task. There exist quite a few data sets for [evaluating the quality of English embeddings](https://github.com/kudkudak/word-embeddings-benchmarks), but this is not the case for other languages or very specialized domains, such as NLP. Moreover, it's unclear what information good embeddings should capture. Should they model syntactic information as well as semantic knowledge? Should they capture semantic similarity, or merely topical relatedness? Often, the answer depends on the end task you want to use the embeddings for. \n",
    "\n",
    "Here we'll use a simple method for evaluating our embeddings. We'll count how many times two nearest neighbours in the vector space have the same part of speech. After all, if our embeddings model similarity (and not just relatedness) in meaning, we expect a noun to have another noun as nearest neighbour, and the same for verbs, adjectives, and so on.\n",
    "\n",
    "First we'll use [spaCy](https://spacy.io/) to determine the part of speech of all the words in our vocabulary. Note that our evaluation metric does rely on the quality of spaCy's part-of-speech tagging, which may not be very accurate for low-frequency words out of context. Nevertheless, we'll assume it's good enough for our purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0c29050d593485d9b2ded3585259805",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3099.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'NOUN'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spacy\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "word2pos = {}\n",
    "for word in tqdm(model.wv.key_to_index):\n",
    "    word2pos[word] = nlp(word)[0].pos_\n",
    "\n",
    "word2pos[\"translation\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we write a simple method that takes a model and looks up the nearest neighbour to every word in its vocabulary. It returns the number of times this nearest neighbour has the same part of speech: a percentage we'll call the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a6ca61b6ef944abb69b0cf6c74dee5a",
       "version_major": 2,
       "version_minor": 0
      },
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       "HBox(children=(FloatProgress(value=0.0, max=3099.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6876411745724427"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def evaluate(model, word2pos):\n",
    "    same = 0\n",
    "    for word in tqdm(model.wv.key_to_index):\n",
    "        most_similar = model.wv.similar_by_word(word, topn=1)[0][0]\n",
    "        if word2pos[most_similar] == word2pos[word]:\n",
    "            same += 1\n",
    "    return same/len(model.wv.key_to_index)\n",
    "       \n",
    "evaluate(model, word2pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we vary some of the settings we introduced above. In particular we're interested in the influence of embedding size (the dimensionality of the trained embeddings), and the size of the context window. We vary the embedding size between 100, 200 and 300,and the context window between 2, 5 and 10. This means we'll train 9 models in total, which obviously takes a bit of time. Feel free to go grab a coffee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size: 100 Window: 2\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, max=3099.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 100 Window: 5\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, max=3099.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 100 Window: 10\n"
     ]
    },
    {
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       "model_id": "0894c8d65a8e43c89dec89dccb5cd4a1",
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 200 Window: 2\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 200 Window: 5\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 200 Window: 10\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=3099.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 300 Window: 2\n"
     ]
    },
    {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 300 Window: 5\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Size: 300 Window: 10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74ebe19acd4a461ebdb6907376c53d6e",
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       "HBox(children=(FloatProgress(value=0.0, max=3099.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>100</th>\n",
       "      <th>200</th>\n",
       "      <th>300</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.709584</td>\n",
       "      <td>0.711197</td>\n",
       "      <td>0.713133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.685705</td>\n",
       "      <td>0.680219</td>\n",
       "      <td>0.681187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.653437</td>\n",
       "      <td>0.646983</td>\n",
       "      <td>0.648596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         100       200       300\n",
       "2   0.709584  0.711197  0.713133\n",
       "5   0.685705  0.680219  0.681187\n",
       "10  0.653437  0.646983  0.648596"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizes = [100, 200, 300]\n",
    "windows = [2,5,10]\n",
    "\n",
    "df = pd.DataFrame(index=windows, columns=sizes)\n",
    "\n",
    "for size in sizes:\n",
    "    for window in windows:\n",
    "        print(\"Size:\", size, \"Window:\", window)\n",
    "        model = gensim.models.Word2Vec(documents, min_count=100, window=window, vector_size=size)\n",
    "        acc = evaluate(model, word2pos)\n",
    "        df[size][window] = acc\n",
    "        \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the accuracies of all models are very similar, the results do show some interesting patterns. \n",
    "\n",
    "First, it looks like smaller contexts work better than larger ones. This is logical, as our evaluation metric is a syntactic one: the closest context words contain much more useful information about the part of speech of a word than those further away. \n",
    "\n",
    "Second, higher-dimensional word embeddings do not necessarily work better than lower-dimensional ones. This may sound counter-intuitive, as higher-dimensional embeddings are able to capture more information. Still, larger embeddings also require more data, while we're using a pretty small corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Word embeddings are one of the most exciting trends on Natural Language Processing since the 2000s. They allow us to model the meaning and usage of a word, and discover words that behave similarly. This is crucial for the generalization capacity of many machine learning models. Moving from raw strings to embeddings allows them to generalize across words that have a similar meaning, and discover patterns that had previously escaped them."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}