10.4 Word embeddings and language models

Word embeddings are like secret codes for words. They turn words into numbers, helping computers understand language better. This magic trick lets machines see how words relate to each other, making it easier to do cool stuff with text.

Language models are the brains behind text understanding and generation. They learn patterns from loads of text data, allowing them to predict words and even create human-like writing. It's like teaching a computer to be a language expert!

Word Embeddings and Applications

Concept and Representation

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• Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation, capturing semantic and syntactic relationships between words in a corpus
• Word embeddings are represented as dense vectors in a high-dimensional space, where the position of a word in the vector space is learned from its usage and context in the training data

Applications and Benefits

• Word embeddings enable various downstream NLP tasks, such as text classification, sentiment analysis, named entity recognition, and machine translation, by providing a meaningful numerical representation of words
• Word embeddings can be used to find word similarities, analogies, and perform mathematical operations on word vectors to uncover semantic relationships
• Popular word embedding models include Word2Vec, GloVe, and FastText, which learn word representations from large text corpora using different training techniques

Training Word Embedding Models

Word2Vec

• Word2Vec is a widely used word embedding model that learns word representations using a shallow neural network architecture, consisting of two main variants: Continuous Bag-of-Words (CBOW) and Skip-gram
  • CBOW predicts the target word based on its surrounding context words, while Skip-gram predicts the surrounding context words given the target word
• Training Word2Vec involves preprocessing the text data, defining the model architecture and hyperparameters (embedding dimension, window size, learning rate), and optimizing the model using techniques like stochastic gradient descent

GloVe and Training Process

• GloVe (Global Vectors for Word Representation) is another popular word embedding model that learns word representations by capturing global word co-occurrence statistics from a corpus
• Training word embedding models involves preprocessing the text data, defining the model architecture and hyperparameters, and optimizing the model using techniques like stochastic gradient descent
• The trained word embedding models can be used to extract word vectors for downstream NLP tasks, either by using pre-trained embeddings or fine-tuning the embeddings on task-specific data
• Evaluation of word embeddings can be done through intrinsic tasks (word similarity, analogy) or extrinsic tasks (using embeddings as features in supervised learning tasks) to assess the quality and effectiveness of the learned representations

Fine-tuning Language Models

Pre-trained Language Models

• Pre-trained language models, such as BERT, GPT, and XLNet, are large-scale neural network models that have been trained on massive amounts of unlabeled text data to capture general language understanding
• These models learn contextual word representations by training on tasks like masked language modeling (predicting masked words in a sequence) and next sentence prediction, allowing them to capture rich semantic and syntactic information

Fine-tuning Process

• Fine-tuning pre-trained language models involves adapting the pre-trained weights to a specific downstream NLP task by adding task-specific layers on top of the pre-trained model and training on labeled task-specific data
• The process of fine-tuning typically involves modifying the model architecture to suit the task requirements, selecting appropriate hyperparameters, and training the model using techniques like transfer learning and gradient descent
• Fine-tuned language models have achieved state-of-the-art performance on various NLP tasks, such as text classification, question answering, named entity recognition, and sentiment analysis

Text Generation and Evaluation

Generating Text with Language Models

• Language models can be used to generate human-like text by predicting the next word or sequence of words based on the given context
• Text generation with language models involves providing a prompt or seed text and iteratively generating the next word or sequence of words based on the learned probability distribution of the model
• Techniques like beam search, top-k sampling, and temperature scaling can be used to control the diversity and coherence of the generated text

Evaluating Generated Text

• Evaluating the quality of generated text is challenging and can be done through various metrics and approaches, such as perplexity, BLEU score, human evaluation, and task-specific metrics
  • Perplexity measures how well the language model predicts the next word in a sequence, with lower perplexity indicating better performance
  • BLEU score is commonly used to evaluate machine translation and text summarization by comparing the generated text with reference human-written text
• Human evaluation involves assessing the generated text based on criteria like fluency, coherence, relevance, and creativity, providing subjective feedback on the quality of the generated output
• Task-specific metrics, such as accuracy, precision, recall, and F1 score, can be used to evaluate the performance of language models on specific NLP tasks like text classification or named entity recognition