Say Goodbye to NLP Errors: Find the Ideal Tokenizer

Tokenization is the fundamental step of breaking down text into smaller, manageable units called tokens. These tokens are the building blocks for various Natural Language Processing (NLP) tasks like text classification, machine translation, and question answering. Selecting the right tokenizer greatly influences the performance of your NLP models. Let’s delve into popular tokenizers and why you might choose one over another.

Types of Tokenizers

Whitespace Tokenization

How it works: Splits text at whitespaces (spaces, tabs, newlines).

• Advantages:
• Very simple and fast.
• Disadvantages:
• Doesn’t handle punctuation well.
• Cannot handle compound words (e.g., “healthcare” might be split into “health” and “care”).

Example (Python-NLTK):

import nltk
text = "This is a sample sentence."
tokens = nltk.word_tokenize(text)
print(tokens)  # Output: ['This', 'is', 'a', 'sample', 'sentence', '.']

Punctuation-Based Tokenization

• How it works: Splits text at both whitespaces and punctuation.
• Advantages: