Text Representation

Methods for converting text into numerical representations for machine learning

Text Representation

Text representation is the process of converting text data into numerical formats that machine learning models can process. This is a crucial step in the NLP pipeline.

Basic Representations

One-Hot Encoding

  • Binary word representation
  • Sparse matrix format
  • Advantages and limitations
  • Implementation considerations

Bag of Words (BoW)

  • Word frequency counting
  • Document-term matrix
  • Term frequency variants
  • Handling vocabulary size

Statistical Methods

TF-IDF

  • Term frequency calculation
  • Inverse document frequency
  • TF-IDF variants
  • Applications and use cases

N-grams

  • Character n-grams
  • Word n-grams
  • Choosing optimal n
  • Handling sparsity

Word Embeddings

Word2Vec

  • CBOW architecture
  • Skip-gram architecture
  • Training process
  • Using pre-trained models

GloVe

  • Global word representations
  • Co-occurrence matrix
  • Training methodology
  • Comparison with Word2Vec

FastText

  • Subword embeddings
  • Handling OOV words
  • Language-specific considerations
  • Performance characteristics

Contextual Embeddings

BERT Embeddings

  • Bidirectional context
  • Token-level embeddings
  • Sentence-level embeddings
  • Fine-tuning strategies

Other Transformer Models

  • GPT embeddings
  • RoBERTa
  • XLNet
  • T5 representations

Document Embeddings

Doc2Vec

  • Paragraph vectors
  • Training methodology
  • Use cases
  • Limitations

Sentence-BERT

  • Sentence embeddings
  • Semantic similarity
  • Cross-lingual capabilities
  • Practical applications

Advanced Techniques

Neural Topic Models

  • LDA alternatives
  • Deep document representations
  • Hierarchical models
  • Evaluation metrics

Cross-lingual Embeddings

  • Alignment methods
  • Zero-shot transfer
  • Multilingual models
  • Applications

Best Practices

  1. Choose appropriate dimensionality
  2. Consider computational resources
  3. Evaluate representation quality
  4. Handle domain-specific vocabulary
  5. Balance accuracy and efficiency

Implementation Considerations

  • Memory management
  • Processing speed
  • Scalability
  • Model size trade-offs

Tools and Frameworks

  • Gensim
  • TensorFlow Text
  • HuggingFace Transformers
  • SpaCy
  • FastAI
  • Text Preprocessing
  • Language Models
  • Neural Networks
  • Dimensionality Reduction