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Capturing semantic meanings using deep learning

Word embedding in natural language processing. Word embedding is a technique that treats words as vectors whose relative similarities correlate with semantic similarity. This technique is one of the most successful applications of unsupervised learning. Natural language processing (NLP) systems traditionally encode words as strings, which are arbitrary and provide no useful information to the system regarding the relationships that may exist between different words.
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