tokenization [Neurosurgery Wiki]

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### **Tokenization in NLP**  

**Tokenization** is the process of breaking down a text into smaller units called **tokens**. These tokens can be words, subwords, characters, or even phrases, depending on the level of granularity required. Tokenization is a fundamental step in **Natural Language Processing (NLP)** and is used in models like **ChatGPT**.

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### **Types of Tokenization**  

#### 1. **Word Tokenization**  
- Splits text into words based on spaces and punctuation.  
- Example:  
  - **Input**: *"The neurosurgeon performs a complex surgery."*  
  - **Tokens**: `["The", "neurosurgeon", "performs", "a", "complex", "surgery", "."]`  

#### 2. **Subword Tokenization (Byte Pair Encoding - BPE, WordPiece, etc.)**  
- Used in models like **GPT** and **BERT**.  
- Breaks words into smaller frequent subunits to optimize the vocabulary size.  
- Example with **BPE**:  
  - **Input**: *"neurosurgery"*  
  - **Tokens**: `["neuro", "surgery"]`  

#### 3. **Character Tokenization**  
- Breaks text into individual characters, useful for languages with long words or no spaces (e.g., Chinese, Japanese).  
- Example:  
  - **Input**: *"ChatGPT"*  
  - **Tokens**: `["C", "h", "a", "t", "G", "P", "T"]`  

#### 4. **Sentence Tokenization (Phrase-based Tokenization)**  
- Segments text into full sentences instead of individual words.  
- Example:  
  - **Input**: *"How are you? Everything is fine!"*  
  - **Tokens**: `["How are you?", "Everything is fine!"]`  

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### **Tokenization in Language Models**  
- **GPT-4** uses a variation of **Byte Pair Encoding (BPE)**, which splits words into frequent subunits to optimize vocabulary size and efficiency.  
- **BERT** uses **WordPiece**, another subword-based method that breaks down words into common segments.  

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### **Example in Python (Using NLTK and SpaCy)**  

You can test tokenization in **Python** using **NLTK** or **SpaCy**:

#### **NLTK Tokenization**  
```python
import nltk
from nltk.tokenize import word_tokenize

nltk.download('punkt')  # Download tokenizer model
text = "The neurosurgeon performs a complex surgery."
tokens = word_tokenize(text)

print(tokens)
# Output: ['The', 'neurosurgeon', 'performs', 'a', 'complex', 'surgery', '.']
```

#### **SpaCy Tokenization**  
```python
import spacy
nlp = spacy.load("en_core_web_sm")

text = "The neurosurgeon performs a complex surgery."
tokens = [token.text for token in nlp(text)]

print(tokens)
```

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### **Applications of Tokenization**
- **Machine Translation** (e.g., Google Translate)  
- **Sentiment Analysis**  
- **Search and Information Retrieval**  
- **Language Models like ChatGPT**  
- **Named Entity Recognition (NER)**