How to tokenize using Gensim in Python?
Gensim is an open-source Python module created for unsupervised topic modeling, document similarity analysis, and natural language processing (NLP).
Tokenization
In natural language processing (NLP) activities, tokenization is critical as it entails breaking up text into discrete tokens, such as words or phrases, to facilitate additional analysis and processing.
Syntax
Below is the syntax for the gensim.utils.tokenize() function:
gensim.utils.tokenize(text, lowercase=True, deacc=False, errors='strict', to_lower=False, lower=False)
textis the input text to be tokenized.lowercaseis an optional parameter that specifies whether to convert the text to lowercase before tokenization. The default value isTrue.deaccis an optional parameter specifying whether to remove text accent marks. The default value isFalse.errorsis an optional parameter that specifies how to handle decoding errors in the text. The default value is'strict'.to_lowerandlowerare both optional parameters that are the same aslowercaseand are used as a convenient alias.
Note: Make sure you have the Gensim library installed (you can install it using pip install gensim)
Tokenize text using Gensim
We will learn a simple and efficient way to tokenize text data using the Gensim library in Python.
Code example
Let's have a look an example of using this function to tokenize text data:
from gensim.utils import tokenizetext = "Welcome to Educative Answers."tokens = list(tokenize(text))print(tokens)
Code explanation
Line 1: Firstly, we import the
tokenizemodule fromgensim.utils.Line 3: Then, we place the text we want to tokenize using a
textvariable.Line 5: Now, we call
tokenize(text)to generate the tokens and then convert the generator object to a list usinglist().Line 7: Finally, we print the tokens to observe the tokenization result.
Output
['Welcome', 'to', 'Educative', 'Answers']
The output shows that the text “Welcome to Educative Answers.” has been successfully tokenized using Gensim. Each word is extracted as a separate token.
Conclusion
Hence, tokenizing text is a fundamental step in NLP tasks, and Gensim provides a convenient way to perform tokenization in Python. By utilizing the gensim.utils.tokenize() function, we can split the text into individual tokens, facilitating further analysis and processing.
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