Sprint Tokenizer

By | October 19, 2022

The Sprint tokenizer is one of the most important components of your Natural language processing pipeline. It is responsible for breaking sentences into tokens, or basically; it breaks the input sentence into different words.

Tokenization can be considered as the first step in NLP because almost every other task requires segmenting a sentence into its individual words to understand it.

The main purpose of a tokenizer is to segment a given text and return an array where each element represents a separate word from that text. There are different ways of tokenizing but in this article, we will learn about how to do it with the help of an NLTK dictionary and regular expressions in Python.

What is the Sprint Tokenizer?

The tokenizer is the first thing that comes to mind when someone mentions preprocessing textual data. It’s a piece of code responsible for breaking down the input into tokens, which are word fragments and other symbols that don’t need any analysis to be processed by the rest of your algorithm.

If you aren’t new to data processing and analysis, you might know that lexers, parsers, and grammars are all parts of parsing algorithms. The tokenizer is not a separate entity but rather an integral part of all these algorithms in one way or another.

What are the Different Types of Tokenizers?

There are three types of tokenizers that you can go for when tokenizing your text. They are –

How to Tokenize with an NLTK Dictionary?

An NLTK dictionary is a pre-defined list of words and their part-of-speech tagging. NLTK already comes with a list of common words and their POS tagging. The code below explains how to use an NLTK dictionary to tokenize your input sentence.

The code above is pretty simple to understand. We are importing a dictionary called nltk.book.porter.dictionary, and then we are using a function called porter_stem to tokenize our input sentence. The porter_stem function returns a list of tokens and each token is a list. The first element in a token list is the original word and the rest are the stems of that word.

How to Tokenize With Regular Expressions?

You can build your regular expression to tokenize your input sentence. In this case, the tokenizer acts like a parsing engine. That is, it breaks the sentence down into individual words based on the regular expression you give it. The code below explains how to do so.

First, we import the regex library from Python.

Then, we create a regex object from the regex pattern we write.

Finally, we call the regex_tokenize function and pass our input sentence as the first argument and our regex object as the second argument. The regex_tokenize function returns a list of tokens and each token is a list. The first element in a token list is the original word and the rest are the stems of that word.

How to Create a Custom Tokenizer?

You can also create your tokenizer if you want something different from NLTK dictionaries and regular expressions. A tokenizer is an object that takes in a string as input and then returns a list of tokens as output.

The code below explains how to create a custom tokenizer.

First, we import collections, and collections. ABC.

Then, we create a class named Tokenizer where every method is a tokenizer. We define our tokenizer by calling the __new__ method of collections. ABC. We pass the Tokenizer class a call method and the arguments we want to be passed to our tokenizer.

Finally, we create an instance of the Tokenizer class named Custom Tokenizer and pass our input sentence as the first argument and the second argument. The Custom  Tokenizer class returns a list of tokens.

Conclusion

The sprint tokenizer is the first step in Natural Language Processing. In this article, you learned about the different types of tokenizers and how to tokenize your text with an NLTK dictionary, regular expressions, and a custom tokenizer.

You can also go through other articles on the Internet to learn more about Natural Language Processing, and you will find that tokenization is the first and most important step in NLP.

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