Definition

Machine learning models inherently work with numerical representations. Tokenization is the bridge that converts raw text data, which is unstructured and messy, into a format computers can actually process.

Tokenization involves splitting text into meaningful units called tokens. These tokens can be:

• Words - "The", "quick", "brown", "fox..."
• Subwords - Word pieces like "jump__ed", allowing models to handle new or out-of-vocabulary words effectively.
• Characters - In some cases, even individual letters.
• Sentences - For tasks where understanding the full context of a sentence is necessary.

Examples

• Sentiment Analysis - Tokenizing a review allows a model to analyze the positive/negative sentiment associated with specific words or phrases.
• Machine Translation - Tokenized sentences in one language are translated into tokens in the target language, using models trained on aligned text pairs.
• Chatbots - Tokenization allows the chatbot to understand the structure of user queries and generate appropriate responses.

Types

• Word-level Tokenization:
  • Simplest form, commonly used.
  • Splits sentences on spaces or punctuation, treating each word as a token.
• Subword Tokenization:
  • Breaks words into smaller units (prefixes, suffixes, roots).
  • Handles rare words and word variations better, leading to more efficient vocabulary representation.
  • Techniques like Byte Pair Encoding (BPE) and WordPiece are popular.
• Character-level Tokenization:
  • Treats individual characters as tokens.
  • Useful for languages without word boundaries or when dealing with very noisy text (like social media data).
• Sentence-level Tokenization:
  • Splits text into complete sentences.
  • Important for tasks like text summarization or question answering, where understanding sentence context is crucial.

How it Helps

• Feature Creation - Tokens become numerical features fed into ML models.
• Vocabulary Building - Tokenization defines the set of unique words or subwords your model will understand.
• Sequence Representation - Preserves word order for tasks where the structure of language matters.
• Model Input - Allows for techniques like word embeddings (e.g., Word2Vec, GloVe) that capture semantic relationships between words.