1. Introduction:
In the domain of NLP, tokenization is the first and important step. Involves split bigger text into smaller text like words, character or sentence called tokens.
Whether you’re developing search engine, chatbot or sentiment analyzer, tokenization is the base.
2. What is Tokenization?
Tokenization is the process of breaking text into smaller text with meaningful units.
3. Installation:
User have to download python and add path in environment variable, once it downloaded then install library using pip command from terminal or code editor.
pip install Tokenization“NLTk Python library tutorial”
Table of Contents:
- Introduction
- What is Tokenization?
- Library Installation
- Types of Tokenization
- Why Tokenization is Important?
- Use Cases
- Challenges
4. Types of Tokenization:
Tokenization is classified into several type based on how text is segmented
- Character Tokenization: In this type of Tokenization, data is breaking and converted to individual characters of sequence
Example- [“India”]
Output: [“I”,”n”,”d”,”i”,”a”]
- Word Tokenization: Word Tokenization is most used method where data is split into individual words
Example- [“Techie Projects”]
Output: [“Techie”,”Projects”]
- Sub Word Tokenization: It is mid of both character and word Tokenization by breaking text into units that are smaller than a full word and larger than a single character.
Example- [“Time”,”table”]
- Sentence Tokenization: Sentence Tokenization also a common method to make a large set of sentence into separated sentence as tokens.
- N-gram Tokenization: N-gram Tokenization break words into fix size chunks of data.
Example- [“AI is powerful”]
Output- [(‘AI’, ‘is’),(‘is’, ‘powerful’)]
5. Why Tokenization is Important?
- Improves model accuracy
- Help in Text Processing
- Essential for:
- Text Classification
- Chatbots
- Sentiment Analysis
6. Use Cases:
– Spam Detection System
– Voice Assistant
– Google Search Engine
7. Challenges:
- Dealing with emojis
- Language specific rules
- Handling punctuation
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