In only a few years, embeddings have changed the world of natural language processing. How far have we come in teaching an artificial brain to understand natural language?
For many decades, computer scientists have been trying to teach computers to understand the human language. It’s a difficult task: Sentences that are easy to understand for us humans can be incredibly complex for machines. A major reason is that humans can see meaning behind words. We know how to put a word in context, know how to reason with it, and know how to use it to give it meaning and relevance. But computer scientists have struggled to teach this sort of deep understanding to a computer. Only recently, a new technique has emerged that promises unprecedented advances: embeddings. In this article I try to shed some light on this new approach in natural language processing.
The problem with one-hot vectors
Many computer programs work with self-learning algorithms: mathematical problem solvers that work with a "numbers in, numbers out" approach. For example, we might be able to ...
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