Human languages are ambiguous; thus there is a high level of complexity in representing, learning, and using linguistic / situational / contextual / word / visual knowledge towards the human language.
Deep LearningMost of these NLP technologies are powered by Deep Learning – a subfield of machine learning.
In contrast, deep learning’s learned features are easy to adapt and fast to learn.
The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side, it covers word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, and convolutional neural networks, as well as some recent models involving a memory component.
They allow deep learning to be effective on smaller datasets, as they are often the first inputs to a deep learning architecture and the most popular way of transfer learning in NLP. The most popular names in word embeddings are Word2vec by Google and GloVe by Stanford.
We then go through each position t in the text, which has a center word c and context words o. Next, we use the similarity of the word vectors for c and o to calculate the probability of o given c. We keep adjusting the word vectors to maximize this probability.
Then we skip one of these words and try to learn a neural network that gets all terms except the one skipped and predicts the skipped term.
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