Implementation of TF-IDF algorithm to find documents similarity with cosine similarity

NLP learning

Trying to learn NLP to use in my projects!

About The Project

There many ways and algorithms to understand language by machines. but first of all we should convert our words to vetcotrs ecause we nedd do to some calulcation on them

Here's some NLP keywords that i have learned till now:

• Using classic AI algorithms like NAIVE Bayes
• using TF-IDF to convert words to vectors
• using word2vec to convert words to vectors

Of course, the list above in not complete but we will epand it in future.

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Built With

This section should list any major frameworks/libraries and tools used implement this project.

• Python3
• Pandas
• Numpy

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Requirements

We used Numpy for it array and math functions

• numpy

  pip install numpy

Run

$ python3 main.py

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Usage

With the TF-IDF algorithm implemented you can find similaroty between different documnets so you can use it in chat bots and search engines.

For more examples, please refer to the Documentation

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License

Distributed under the MIT License. See LICENSE.md for more information.

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Contact

Faraz Farangizadeh - [email protected]

Project Link: https://github.com/farazff/NLP-Learning

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