Project
Readability Score (Python)
Hard
128 completions
~ 17 hours
4.0Learn to call programs from the command line and gain experience with the nltk and math libraries.
Provided by
JetBrains Academy
About
We all love different kinds of books. As we grow up, we are able to digest more difficult texts easier. How to assess the difficulty of a given text? How can you make your program do that? Write a program that determines the difficulty of a text and predicts the grade level needed to understand this text.
Graduate project
This project covers the core topics of the Introduction to Natural Language Processing course, making it sufficiently challenging to be a proud addition to your portfolio.
At least one graduate project is required to complete the course.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Input a phrase or a sentence for the program to determine the difficulty based on the count of symbols and sentences.
Apply your knowledge of strings and lists. Determine the text difficulty by calculating the average word count in the sentences.
Add a readability score based on a special formula. Estimate the difficulty of larger texts uploaded from a file.
Let's implement more variables into our score formulas — syllables.
Use a comprehensive formula, based on the amount of difficult (rare) words, taken from the corpora.
Reviews
DL
Daniel Lopez3 months ago
I have learned how to solve this without the NLTK module because it was giving me errors.
3 months ago
In this project, I learned how to calculate different readability scores using Python, including the Automated Readability Index, the Flesch-Kincaid test, and the Dale-Chall index. I practiced reading files from command line arguments, using regular expressions to count words, sentences, characters, ...
1 year ago
The Text Readability Score Analyzer is a robust Python tool that integrates multiple established formulas to assess text complexity. Its detailed metrics, from syllable counts to vocabulary difficulty, provide precise age-based comprehension insights. The clean design, comprehensive features, and pr ...
4.0
Learners who completed this project within the Introduction to Natural Language Processing course rated it as follows: