14 Mar NLP – Tokenization

Tokenization is breaking text into smaller pieces, like words or sentences. For example, “I love NLP” becomes [“I”, “love”, “NLP”].

Tokenization is one of the first and most important steps in Natural Language Processing (NLP). It’s like breaking a sentence into individual pieces (or “tokens”) so that computers can understand and process it. Let’s break it down in a simple way.

What is Tokenization

Tokenization is the process of splitting text into smaller units called tokens. These tokens can be:

• Words: For example, splitting “I love NLP” into [“I”, “love”, “NLP”].
• Sentences: For example, splitting a paragraph into individual sentences.
• Subwords or Characters: Sometimes, even smaller units are used for specific tasks.

Why is Tokenization Important

Computers don’t understand text the way humans do. They need text to be broken down into smaller, manageable pieces so they can analyze it. Tokenization helps by:

1. Simplifying Text: Breaking it into smaller parts makes it easier to process.
2. Enabling Analysis: Tokens are the building blocks for tasks like counting words, finding patterns, or training machine learning models.
3. Handling Different Languages: Tokenisation rules can be adapted for different languages and writing systems.

How Does Tokenization Work

Let’s look at some examples of tokenization in action:

1. Word Tokenisation

This is the most common type of tokenization. It splits text into individual words. For example:

• Input: “I love NLP!”
• Output: [“I”, “love”, “NLP”, “!”]

2. Sentence Tokenisation

This splits text into individual sentences. For example:

• Input: “I love NLP. It’s so cool!”
• Output: [“I love NLP.”, “It’s so cool!”]

3. Subword Tokenisation

This breaks words into smaller pieces, which is useful for languages with complex words or for machine learning models. For example:

• Input: “unhappiness”
• Output: [“un”, “happiness”]

4. Character Tokenisation

This splits text into individual characters. For example:

• Input: “NLP”
• Output: [“N”, “L”, “P”]

Challenges in Tokenisation

Tokenization sounds simple, but it can get tricky because language is messy. Here are some challenges:

1. Punctuation

Should punctuation be treated as separate tokens or attached to words? For example:

• “I love NLP!” → Should the exclamation mark be a separate token (“!”) or part of the word (“NLP!”)?

2. Contractions and Abbreviations

How should contractions and abbreviations be split? For example:

• “I’m” → Should it be split into [“I”, “am”] or kept as one token (“I’m”)?
• “U.S.A.” → Should it be split into [“U”, “.”, “S”, “.”, “A”, “.”] or kept as one token (“U.S.A.”)?

3. Hyphenated Words

How should hyphenated words be handled? For example:

• “state-of-the-art” → Should it be split into [“state”, “of”, “the”, “art”] or kept as one token (“state-of-the-art”)?

4. Languages Without Spaces

Some languages, like Chinese or Japanese, don’t use spaces between words. Tokenizing these languages requires special techniques. For example:

• Input (Chinese): “我爱NLP” (I love NLP)
• Output: [“我”, “爱”, “NLP”]

Example of Tokenisation in Action

Let’s say you have the following text:
“Hello, world! I’m learning NLP. It’s so COOL!! 😊”

After tokenization, it might look like this:
[“Hello”, “,”, “world”, “!”, “I”, “’m”, “learning”, “NLP”, “.”, “It”, “’s”, “so”, “COOL”, “!”, “!”, “😊”]

Why Is Tokenization Used?

Tokenization is a crucial step in many NLP tasks, such as:

1. Text Analysis: Counting words, finding patterns, or analyzing sentiment.
2. Machine Learning: Preparing text data for training models.
3. Search Engines: Breaking down queries into searchable terms.
4. Translation: Splitting text into units for translation.

Tokenization is also used in programming languages to break code into tokens like keywords, variables, and operators. For example, in the line “print(‘Hello’)”, the tokens might be [“print”, “(“, “‘Hello'”, “)”].

In short, tokenization is like chopping text into bite-sized pieces so computers can understand and process it. It’s a simple but powerful step that makes all the other NLP tasks possible.

If you liked the tutorial, spread the word and share the link and our website Studyopedia with others.

For Videos, Join Our YouTube Channel: Join Now

Read More:

• Generative AI Tutorial
• Machine Learning Tutorial
• Deep Learning Tutorial
• Ollama Tutorial
• Retrieval Augmented Generation (RAG) Tutorial
• Copilot Tutorial
• Gemini Tutorial
• ChatGPT Tutorial