Скачать или смотреть How to Extract N-Gram Suffixes Using Scikit-Learn's CountVectorizer

Learn how to efficiently extract `n-gram suffixes` from words using Scikit-Learn's CountVectorizer and how to use these n-grams as features in your machine learning models.
---
This video is based on the question https://stackoverflow.com/q/64385830/ asked by the user 'Praneeth Vasarla' ( https://stackoverflow.com/u/8432601/ ) and on the answer https://stackoverflow.com/a/64386141/ provided by the user 'yatu' ( https://stackoverflow.com/u/9698684/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Getting n gram suffix using sklearn count vectorizer

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Unlocking the Power of N-Gram Suffixes with Scikit-Learn

In the world of Natural Language Processing (NLP), extracting meaningful features from text is crucial for building robust machine learning models. One interesting approach is utilizing n-gram suffixes, which can enhance the representation of words in your dataset. If you've ever wondered how to obtain these suffixes efficiently using Scikit-Learn's CountVectorizer, then you’re in the right place!

The Problem: Extracting N-Gram Suffixes

Imagine you are working with words, such as "Apple". Your goal is to extract its suffixes, which can be represented in n-grams. For example:

1-gram suffix: 'e'

2-gram suffix: 'le'

3-gram suffix: 'ple'

While Scikit-Learn's CountVectorizer is typically used to generate all n-grams from text, you may want to focus solely on the suffixes. This can be a bit tricky if you are new to NLP and machine learning. Let’s dive into a straightforward solution that makes this task achievable!

The Solution: Custom Analyzer in CountVectorizer

To extract n-gram suffixes, you can define a custom analyzer in the CountVectorizer. By implementing a simple lambda function, you can specify how the features (n-gram suffixes) are obtained from the input words. Here’s how you can do it:

Step-by-Step Implementation

Import the Required Libraries

First, ensure you have the necessary libraries imported. You will need CountVectorizer from sklearn.feature_extraction.text, and you might also want to import pandas for data manipulation.

[[See Video to Reveal this Text or Code Snippet]]

Define Your Words and Set N

Define a list of words you would like to extract n-gram suffixes from. In this example, let’s use ["Orange", "Apple", "I"] and set n as 3.

[[See Video to Reveal this Text or Code Snippet]]

Create the CountVectorizer with Custom Analyzer

Utilize the CountVectorizer, passing a custom lambda function that will retrieve the suffixes for the specified range of n. Here’s how:

[[See Video to Reveal this Text or Code Snippet]]

Convert the Resulting Matrix into a DataFrame

Finally, to view and utilize the extracted suffixes, convert the matrix into a Pandas DataFrame:

[[See Video to Reveal this Text or Code Snippet]]

When you run this code, you will see an organized DataFrame that looks like this:

Iegelengeple001101010101012100000Using N-Grams as Features in Machine Learning

Once you have extracted the n-gram suffixes and converted them into a numerical representation, you can effortlessly incorporate these features into your machine learning models. Here's how:

Data Preparation: Make sure to include your DataFrame with the n-gram suffixes in your feature set.

Model Selection: Choose an appropriate machine learning model based on your problem. This might include algorithms such as Logistic Regression, Random Forests, or Neural Networks.

Training the Model: Use your n-gram features to train the model—fit it to your training data and evaluate it with your test data.

Conclusion

Extracting n-gram suffixes using Scikit-Learn's CountVectorizer is a practical way to enhance your NLP projects. By leveraging a custom analyzer, you can easily focus on specific aspects of your data, and using these n-grams as features will enable your models to perform better. Happy coding, and may your NLP journey be fruitful!