Summary of A Complete Overview of Word Embeddings

Summary of "A Complete Overview of Word Embeddings"

This video provides a comprehensive explanation of word embeddings, their purpose, creation methods, and practical usage in natural language processing (NLP).

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Main Ideas and Concepts

1. Why Word Embeddings Are Needed
   • Machine learning models cannot work directly with raw text; they require numerical input.
   • Text must be converted into numerical representations.
   • Traditional methods include:
     • One-hot encoding: Creates a sparse, high-dimensional vector with mostly zeros.
     • Count-based approaches: Summarize sentences into vectors by counting word occurrences.
       • Examples: Bag of Words, n-grams, TF-IDF.
   • These traditional methods have limitations:
     • They do not capture word context.
     • Cannot handle unseen words.
     • Result in sparse, inefficient vectors.
2. What Are Word Embeddings?
   • Word embeddings represent words as dense, low-dimensional vectors.
   • Similar words (used in similar contexts) have vectors close to each other in the embedding space.
   • Embedding space is a multi-dimensional vector space where distances reflect semantic similarity.
   • Example: Vectors for "tea" and "coffee" are closer than "tea" and "pea" despite spelling similarities.
   • Embeddings enable meaningful vector operations (e.g., king - man + woman ≈ queen).
3. How Word Embeddings Are Created
   • Word embeddings are learned from large text corpora.
   • Approaches include:
     • Embedding layer in neural networks: Initialized randomly and trained alongside the main model.
       • Advantage: Custom and task-specific embeddings.
       • Disadvantage: Requires large data and long training.
     • Pre-trained embedding models: Trained on large datasets and can be reused.
   • Popular embedding algorithms:
     • Word2Vec: Uses context to predict words.
       • Two methods:
         • Continuous Bag of Words (CBOW): Predict middle word from surrounding words.
         • Skip-gram: Predict surrounding words from the middle word.
       • Single hidden layer neural network; embedding size = number of neurons.
     • GloVe (Global Vectors): Combines local context (like Word2Vec) with global co-occurrence statistics.
       • Objective: Dot product of word vectors approximates log of word co-occurrence probabilities.
     • FastText: Extends Word2Vec by representing words as subword (character n-grams) units.
       • Handles rare and unseen words better.
       • Works well with morphologically rich languages.
     • ELMo (Embeddings from Language Models):
       • Contextual embeddings: word vectors depend on entire sentence context.
       • Uses bi-directional LSTM trained on language modeling tasks.
       • Can differentiate homonyms and handle misspellings.
       • More complex and requires dedicated explanation.
4. Using Word Embeddings in Projects
   • Two main options:
     • Train your own embeddings: Custom to your data but resource-intensive.
     • Use pre-trained embeddings: Saves time, not always domain-specific.
   • Pre-trained embeddings can be used:
     • Statically (fixed during training).
     • Fine-tuned (updated during training).
   • Libraries like Gensim provide easy access to pre-trained embeddings (Word2Vec, GloVe, FastText).
   • Practical exploration:
     • Checking closest words to a query word.
     • Measuring similarity between words.
     • Performing analogy tasks (e.g., king - man + woman = queen).
     • Example analogy with restaurant - dinner + cocktail = bar (varies by model).
   • Word embeddings are typically used as part of larger NLP models for tasks like sentiment analysis.

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Methodology / Instructions Highlighted

• One-hot encoding:
  • Create a vector as long as the vocabulary.
  • Set the index corresponding to the word to 1, others to 0.
• Count-Based Approaches:
  • Bag of Words: Count word occurrences ignoring order.
  • N-grams: Count occurrences of contiguous sequences of n words.
  • TF-IDF: Weight words by frequency in document vs. corpus.
• Word2Vec Training:
  • Slide a window of n words over text.
  • CBOW: Use context words to predict center word.
  • Skip-gram: Use center word to predict context words.
  • Train a single-layer neural network.
  • Extract embeddings from weights after training.
• Using Pre-trained Embeddings:
  • Install library (e.g., Gensim).
  • Load pre-trained models (e.g., Word2Vec, GloVe, FastText).
  • Query nearest neighbors or perform vector arithmetic.
  • Use embeddings statically or fine-tune during model training.

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Speakers / Sources Featured

• Unnamed Narrator / Presenter

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Educational

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