Machine Learning algorithms: Working with text data - JAXenter

JAXenter: What is the difference between image and text from a machine's point of view? Christoph Henkelmann: Almost all ML methods, especially neural networks, want tensors (multidimensional arrays of numbers) as input. In case of an image the transformation is obvious, we already have a three-dimensional array of pixels (width x height x color channel), i.e. except for smaller pre-processing the image is already "bite-sized". There is no obvious representation for text. Text and words exist at a higher level of meaning, for example, if you simply enter Unicode-encoded letters as numbers in the net, the jump from coding to semantics is too "high".