Contents
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"Many thanks to the exploration of Doc2Vec done by [Marton Trencseni](https://bytepawn.com/similar-posts-recommendation-with-doc2vec.html), on which many of these aproaches are based."
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"cell_type": "markdown",
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"id": "af8da066",
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"metadata": {},
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"source": [
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"Contents: \n",
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"\n",
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"- [Embedding the text](#embedding)\n",
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"- [Similar initiatives](#similar)\n",
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"- [Find Clusters](#clusters)\n",
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"- [Alternative Maps: Rendering Embedding Space](#rendering)\n",
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"- [A situated map](#map)\n"
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]
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"cell_type": "markdown",
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"id": "aaac4f97",
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"metadata": {},
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"source": [
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"## 1. Embedding the text\n",
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"## 1. Embedding the text <a name=\"embedding\"></a>\n",
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"\n",
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"In this first step, the descriptions of the initiatives are embedded using Doc2Vec. That is to say, in a series of steps the text of the descriptions is transformed into a vector, a point in a (latent) multidimensional space: an embedding."
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"id": "2e6b9b9d",
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"metadata": {},
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"source": [
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"## 2. Similar initiatives\n",
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"## 2. Similar initiatives <a name=\"similar\"></a>\n",
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"\n",
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"Now that we have a Doc2Vec model, we can use it to calculate some sort of similarity score between the initiative in our little dataset which is based on a mathematical similarity of words ocuring in their descriptions. "
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]
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"id": "562d4340-e4b0-4366-a917-ba91056c8ced",
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"metadata": {},
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"source": [
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"## 3. Find clusters"
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"## 3. Find clusters <a name=\"clusters\"></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "256a6c0a",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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"cell_type": "markdown",
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"id": "6f68a1c0",
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"id": "fa10d87a-4435-4e8c-bde1-96861d4401ad",
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"metadata": {},
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"source": [
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"## 4. Rendering embedding space: alternative maps of movement"
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"## 4. Rendering embedding space: alternative maps of movement <a name=\"rendering\"></a>"
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]
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},
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{
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"id": "70b3afe8",
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"metadata": {},
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"source": [
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"## 5. A situated map"
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"## 5. A situated map <a name=\"map\"></a>"
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]
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},
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{
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