13.1 Word embeddings and language models

Word embeddings revolutionized NLP by representing words as dense vectors. These compact representations capture semantic relationships, enabling machines to understand language nuances. They're the foundation for many NLP tasks, from sentiment analysis to machine translation.

Language models take word embeddings further, predicting words based on context. RNNs and transformers are key architectures here. While RNNs excel at sequential data, transformers like BERT and GPT have become game-changers, powering advanced applications in text generation and understanding.

Word Embeddings

Word embeddings in NLP

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• Dense vector representations of words capture semantic and syntactic information
• Convert words to numerical format for machine learning models represent words in continuous vector space
• Low-dimensional vectors (typically 50-300 dimensions) learned from large text corpora
• Capture word similarities and relationships reduce dimensionality compared to one-hot encoding enable transfer learning in NLP tasks
• Used in text classification, named entity recognition, machine translation, and sentiment analysis

Implementation of word2vec and GloVe

• Word2vec model utilizes two main architectures: Continuous Bag of Words (CBOW) and Skip-gram
• Training process uses context words to predict target word (CBOW) or target word to predict context words (Skip-gram)
• Negative sampling technique improves training efficiency
• GloVe model based on word co-occurrence statistics minimizes difference between dot product of word vectors and log of co-occurrence probability
• Training process creates co-occurrence matrix factorizes matrix to obtain word vectors
• Evaluation methods include intrinsic evaluation (word similarity tasks, analogy tasks) and extrinsic evaluation (performance on downstream NLP tasks)

Language Models

Language models with RNNs vs transformers

• RNNs use hidden state to capture sequential information with input, output, and recurrent connections
• RNN variants include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
• RNN training process uses Backpropagation Through Time (BPTT) and Truncated BPTT for long sequences
• Transformer architecture employs self-attention mechanism, multi-head attention, positional encoding, and feed-forward neural networks
• Transformer training process involves masked language modeling and next sentence prediction
• Evaluation metrics include perplexity, BLEU score (machine translation), and ROUGE score (text summarization)

Applications of BERT and GPT

• BERT uses bidirectional context understanding pre-trained with Masked Language Modeling (MLM) and Next Sentence Prediction (NSP)
• GPT employs unidirectional (left-to-right) language modeling with generative capabilities
• Transfer learning in NLP uses pre-trained models as feature extractors or fine-tunes them for specific tasks
• Pre-trained models applied to text classification, named entity recognition, question answering, and text generation
• Challenges include computational resources required, domain-specific fine-tuning, and ethical considerations in using large language models