Tag: Tokens In NLP

Tokens are the basic units we get when we split a piece of text, like a sentence, into smaller parts. In natural language processing (NLP), this process is called tokenization. Typically, tokens are words, but they can also be punctuation marks or numbers—basically anything that appears in the text.

Tokenizing “I love you 3000”

When we tokenize the sentence “I love you 3000,” we split it into its individual components. Using a standard tokenizer (like the one from Python’s NLTK library), the result would be:

• “I”
• “love”
• “you”
• “3000”

So, the tokens are: “I”, “love”, “you”, “3000”.

Are Tokens Text or Numbers?

Now, to the core question: are these tokens always text, or can they be numbers? In the tokenization process, tokens are always text, meaning they are sequences of characters (strings). Even when a token looks like a number, such as “3000,” it is still treated as a string of characters: “3”, “0”, “0”, “0”.

For example:

• In Python, if you tokenize “I love you 3000” using NLTK:

import nltk
sentence = "I love you 3000"
tokens = nltk.word_tokenize(sentence)
print(tokens)

The output is: [‘I’, ‘love’, ‘you’, ‘3000’]. Here, “3000” is a string, not an integer.

Can Tokens Represent Numbers?

Yes, tokens can represent numbers! The token “3000” is made up of digits, so it can be interpreted as the number 3000. However, during tokenization, it remains a text string. If you want to use it as an actual numerical value (like an integer or float), you’d need to convert it in a separate step after tokenization. For instance:

• Convert “3000” to an integer: int(“3000”) in Python, which gives you the number 3000.

What If I Want Numbers?

If your goal is to work with “3000” as a number (not just a string), tokenization alone won’t do that. After tokenizing, you can:

1. Identify which tokens are numbers (e.g., check if they consist only of digits).
2. Convert them to numerical types (e.g., int(“3000”)).

For example:

• Tokens: [“I”, “love”, “you”, “3000”]
• After conversion: You could process “3000” into the integer 3000 while leaving the other tokens as text.

In the sentence “I love you 3000,” the tokens are all text: “I”, “love”, “you”, “3000”. The token “3000” is a string that represents a number, but as a token, it’s still text. Tokens are always text in the sense that they are sequences of characters produced by tokenization. If you need them to be numbers for some purpose, that’s a step you’d take after tokenization.

So, to answer directly: tokens are always text, but they can represent numbers if they’re made of digits, like “3000” in this example.