Bag of Words

Key Ideas and Concepts Summary:

1. Goal of Language Representation:

   • Convert unstructured text into structured numerical form so computers can process, analyze, and generate language.

2. Evolution of Language Representation Methods:

   • Bag of Words (BoW):

     • Represents text as sparse vectors based on word counts.

     • Ignores word order and context.

     • Involves tokenization (splitting text into tokens) and creating a vocabulary (unique tokens across texts).

     • Produces vector representations that record word frequency.

   • Word2Vec:

     • Improves on BoW by capturing semantic meaning using context from neighboring words.

     • Produces dense vectors that reflect word relationships.

   • Transformers:

     • Modern approach capturing word meaning in full sentence or paragraph context.

     • Use attention mechanisms for deeper understanding of relationships between words.

3. Types of Language Models:

   • Encoder-only models: Focus on understanding and representing text numerically (e.g., BERT).

   • Decoder-only models: Focus on generating text (e.g., GPT).

   • Encoder-decoder models: Combine both understanding and generation capabilities (e.g., T5).

4. Importance of Early Models:

   • Non-transformer methods like BoW and Word2Vec laid the groundwork for current transformer-based models.

   • Still useful as baselines for simple language processing tasks.

5. Core Concept — Vector Representations:

   • Text is converted into numerical vectors for computation.

   • In BoW, numbers represent word counts; in advanced models, vector values encode meaning and context.

6. Overall Message:

   • The history of language AI reflects a progression from simple word counting to complex contextual understanding, forming the foundation for today’s advanced transformer models.