With deep learning and dimensionality reduction, we can visualize the entirety of Wikipedia?

Deep neural networks are an approach to machine learning that has revolutionized computer vision and speech recognition in the last few years, blowing the previous state of the art results out of the water. They’ve also brought promising results to many other areas, including language understanding and machine translation. Despite this, it remains challenging to understand what, exactly, these networks are doing.


Understanding neural networks is just scratching the surface, however, because understanding the network is fundamentally tied to understanding the data it operates on. The combination of neural networks and dimensionality reduction turns out to be a very interesting tool for visualizing high-dimensional data – a much more powerful tool than dimensionality reduction on its own.


Paragraph vectors, introduced by Le & Mikolov (2014), are vectors that represent chunks of text. Paragraph vectors come in a few variations but the simplest one, which we are using here, is basically some really nice features on top of a bag of words representation.


With word embeddings, we learn vectors in order to solve a language task involving the word. With paragraph vectors, we learn vectors in order to predict which words are in a paragraph.


Concretely, the neural network learns a low-dimensional approximation of word statistics for different paragraphs. In the hidden representation of this neural network, we get vectors representing each paragraph. These vectors have nice properties, in particular that similar paragraphs are close together.


Now, Google has some pretty awesome people. Andrew Dai, Quoc Le, and Greg Corrado decided to create paragraph vectors for some very interesting data sets. One of those was Wikipedia, creating a vector for every English Wikipedia article. The result is that we get a visualization of the entirety of Wikipedia. A map of Wikipedia. A large fraction of Wikipedia’s articles fall into a few broad topics: sports, music (songs and albums), films, species, and science.

Sourced through Scoop.it from: colah.github.io

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