Tokenization

Tokenization is the process of breaking down text or data into smaller units called tokens. These tokens can be words, phrases, symbols, or other meaningful elements depending on the context. In natural language processing (NLP), tokenization typically involves splitting sentences into words or subwords to enable easier analysis and processing by algorithms.

In a broader sense, tokenization also refers to the transformation of sensitive information, like credit card numbers or personal data, into non-sensitive placeholders called tokens in the field of data security. However, within artificial intelligence and text processing, tokenization primarily serves as a foundational step that converts raw text into a structured format understandable by machine learning models.

For example, the sentence "Tokenization is essential for NLP." can be tokenized into tokens as ["Tokenization", "is", "essential", "for", "NLP", "."]. These tokens are then used to build models for tasks such as text classification, translation, or sentiment analysis.

Tokenization involves several technical steps that depend on the specific application and language:

1. Text Segmentation

The raw input text is segmented into tokens using rules or algorithms. For languages with clear word boundaries like English, spaces and punctuation often define token boundaries.

2. Handling Special Cases

Tokenizers handle punctuation, contractions, numbers, and special characters carefully. For example, "don't" might be split into ["do", "n't"] or kept as one token depending on the tokenizer.

3. Subword Tokenization

In advanced NLP models, subword tokenization breaks down rare or complex words into smaller pieces (subwords) to improve vocabulary coverage. Algorithms like Byte Pair Encoding (BPE) or WordPiece are commonly used.

4. Output Tokens

The output is a sequence of tokens that serve as input for further tasks such as vectorization or embedding generation.

1. Input: Raw text string
2. Segmentation: Split by spaces, punctuation, or learned subwords
3. Normalization: Optional lowercasing, stemming, or removing stopwords
4. Output: Array or sequence of tokens

The process optimizes the representation of text so that computational models can analyze semantic and syntactic structures more effectively.

Common Use Cases of Tokenization

• Natural Language Processing (NLP): Tokenization breaks down sentences into words or subwords to enable text parsing, sentiment analysis, machine translation, and chatbot responses.
• Information Retrieval: Search engines tokenize documents and queries to match relevant results effectively by comparing tokens.
• Text Preprocessing for Machine Learning: Tokenization is the first step before generating word embeddings or feeding data into language models.
• Data Masking and Security: In the context of data protection, sensitive information like credit card numbers is replaced by secure tokens for safe processing without exposing actual data.
• Speech Recognition Systems: Tokenization helps convert recognized speech into meaningful word tokens for transcription and further analysis.