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Would you care to elaborate? My main doubt comes the fact that language meaning may vary between different contexts, but I am no expert and am earnestly curiou

• JacobiX 9y ago
  Vector representation are useful for many natural language processing tasks. In word embedding like word2vec, GLoVE, fasttext for each word the algorithm learns an associated vector in dimension n (x1, x2, .., xn). A word maybe close to another in one dimension (or a subspace) but far away in another one. Moreover good representation allows meaningful vector space arithmetic: Queen - Women == King word representations are typically trained on very large unlabeled data, but once the algorithm learns the features you can use them for your small dataset. EDIT: Add more explanations.
• rjtavares 9y ago
  This blog post[1] is a great example of positive unintended consequences of deep learning: > We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment. [1]: https://blog.openai.com/unsupervised-sentiment-neuron/ https://blog.openai.com/unsupervised-sentiment-neuron/