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NLP Demystified 12: Capturing Word Meaning with Embeddings

Course playlist:    • Natural Language Processing Demystified   We'll learn a method to vectorize words such that words with similar meanings have closer vectors (aka "embeddings"). This was a breakthrough in NLP and boosted performance on a variety of NLP problems while addressing the shortcomings of previous approaches. We'll look at how to create these word embeddings and how to use them in our models. Colab notebook: https://colab.research.google.com/git... Timestamps 00:00:00 Word Vectors 00:00:37 One-Hot Encoding and its shortcomings 00:02:07 What embeddings are and why they're useful 00:05:12 Similar words share similar contexts 00:06:15 Word2Vec, a way to automatically create word embeddings 00:08:08 Skip-Gram With Negative Sampling (SGNS) 00:17:11 Three ways to use word vectors in models 00:18:48 DEMO: Training and using word vectors 00:41:29 The weaknesses of static word embeddings This video is part of Natural Language Processing Demystified --a free, accessible course on NLP. Visit https://www.nlpdemystified.org/ to learn more.