NLP Tokenization

- Overview

NLP tokenization is the process of breaking down a stream of text into smaller, meaningful units called tokens, which can be words, sub-words, or sentences. It's a fundamental first step in Natural Language Processing (NLP) that makes raw text usable for machines, preparing it for tasks like language modeling, machine translation, and text analysis. 

1. How tokenization works: 

• Splitting text: A tokenizer divides a sentence like "What restaurants are nearby?" into individual tokens: "what", "restaurants", "are", "nearby", and "?".
• Handling complexity: It involves rules to manage punctuation, hyphens, and other edge cases, ensuring consistent and accurate segmentation.
• Token types: Tokens can be words, but advanced methods also use sub-word units to handle rare words or create a more manageable vocabulary.

2. Why tokenization is important:

• Foundation for NLP: It is a crucial initial step for almost all NLP tasks.
• Enables machine understanding: By converting text into tokens, it allows computers to process and analyze language computationally.
• Prepares for further steps: The list of tokens is then used in subsequent steps, such as converting tokens to numerical representations (embeddings) for machine learning models.

3. Types of tokenization:

• Word tokenization: Splits text into words and punctuation.
• Sentence tokenization: Splits a text into individual sentences.
• Sub-word tokenization: Breaks rare or large words into smaller, more frequent sub-word units, like splitting "unhappiness" into "un" and "happiness".

- Tokenization in Detail:

Tokenization is a crucial preprocessing step in NLP. It involves dividing a continuous stream of text into discrete units called tokens. These tokens can be words, punctuation marks, numbers, or even subword units, depending on the chosen tokenization method. 

The purpose is to simplify complex human language into manageable parts that can be easily processed and analyzed by computers. 

This foundational step enables subsequent NLP tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis by providing a structured representation of the text. 

Example of Tokenization:

- Natural Language Processing (NLP) and Tokenization

Natural Language Processing (NLP) is a field within artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. It combines computational linguistics with machine learning and deep learning techniques to bridge the communication gap between humans and machines. 

A. Key Concepts of NLP:

1. Understanding Human Language: NLP allows machines to process and analyze human language in both written and spoken forms, moving beyond simple symbol recognition to grasp meaning and context. 

2. Core Components: 

NLP utilizes various components and methodologies, including:

• Tokenization: The process of breaking down text into smaller units (tokens) like words, punctuation, or subwords, forming the foundational step for further analysis.
• Part-of-Speech Tagging: Assigning grammatical categories (e.g., noun, verb, adjective) to words.
• Named Entity Recognition: Identifying and classifying named entities such as people, organizations, and locations.
• Sentiment Analysis: Determining the emotional tone or sentiment expressed in text.

3. Applications: NLP drives numerous applications, including:

4. Challenges: 
NLP faces challenges due to the complexity and evolving nature of human language, including:

• Ambiguity: Dealing with words or phrases that have multiple meanings depending on context.
• Data bias: Ensuring fairness and representativeness in training data to avoid biased outcomes.
• Low-resource languages: Providing adequate support for languages with limited available data.

- NLP Tokenization Techniques

NLP tokenization techniques break text into smaller units (tokens) like words, subwords, or characters, essential for models to process language, ranging from simple whitespace/punctuation splitting (word tokenization) to advanced methods like Byte Pair Encoding (BPE) or WordPiece, used in models like BERT, to handle rare words and manage vocabulary size efficiently by creating subword units, with sentence tokenization splitting paragraphs into sentences as another key type. 

1. Basic Techniques

• Whitespace Tokenization: Splits text by spaces, simple but struggles with punctuation attached to words.
• Punctuation-Based: Splits on periods, commas, etc., but needs rules for abbreviations (e.g., "Mr.").
• Sentence Tokenization: Divides text into sentences using delimiters like periods, question marks.

2. Advanced Techniques (Subword Tokenization) 

These methods balance vocabulary size with meaningful units, crucial for modern LLMs.

• Byte Pair Encoding (BPE): Iteratively merges the most frequent character pairs into new tokens, creating subwords (e.g., "tokenization" -> "token", "iz", "ation").
• WordPiece (used in BERT): Similar to BPE but merges based on likelihood of forming a word, adding ## for subwords (e.g., "playing" -> "play", "##ing").
• Unigram Language Model: Starts with a large vocabulary and prunes less useful tokens based on probability.
• SentencePiece: Handles text without pre-tokenization (like whitespace), working directly on raw text for multilingual support.

3. Rule-Based & Dictionary-Based:

4. Key Considerations: