SmartTextAI

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  Output

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  output

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  flashcards

Inspiration

The inspiration behind our project stems from the need to create a versatile language toolkit that caters to various language processing tasks. We envisioned a single platform capable of handling keyword extraction, spelling correction, and next word prediction. This project was motivated by the desire to empower users with a comprehensive tool for enhancing their language skills and aiding them in tasks like content creation, proofreading, and writing assistance.

What it does

Keyword Extraction:It identifies and extracts the most relevant keywords from a given text, providing users with a quick summary of the main themes and topics. Spelling Correction: The toolkit corrects spelling errors in a given text, improving overall text quality and aiding users in producing error-free content. Next Word Prediction:By utilizing n-gram models, the toolkit predicts the next word in a sequence based on user input, assisting users in generating coherent and contextually relevant sentences. Enables language learning through interactive flashcards, randomly presenting words for translation practice. The user is prompted to provide the translation, promoting an engaging and effective learning experience.

How we built it

We utilized Python for the implementation of our language learning tools. For Keyword Extraction, we employed the rake_nltk library, leveraging its keyword extraction capabilities. In Spelling Correction, we harnessed the SpellChecker module to correct misspelled words in a given text. Additionally, for Next Word Prediction, we implemented a simple n-gram model using regular expressions and defaultdict. The main program serves as a user-friendly interface to interact with these language learning functionalities.

Challenges we ran into

While developing the project, we faced challenges in integrating the different language processing functionalities seamlessly. Incorporating the spelling correction module and the next word prediction model into the main program required careful attention to avoid conflicts and ensure smooth execution. Additionally, handling user inputs and providing a clear user interface posed some challenges, but we overcame them through iterative testing and refinement.

Accomplishments that we're proud of

We are proud to have successfully integrated multiple language processing features, including keyword extraction, spelling correction, and next word prediction, into a single cohesive program. This holistic approach allows users to seamlessly switch between different functionalities within the same interface, enhancing the overall user experience. Additionally, we take pride in creating an interactive and user-friendly design that accommodates various language-related tasks in a unified application.

What we learned

Throughout the development of this project, we gained valuable insights into natural language processing techniques and tools. Our team deepened its understanding of keyword extraction methods using the Rake algorithm, explored spelling correction functionalities using the SpellChecker library, and implemented next word prediction through n-gram modeling. We also learned how to design a versatile and interactive command-line interface that accommodates diverse language-related tasks. This project provided hands-on experience in integrating different language processing features seamlessly.

What's next for SmartTextAI

In the future, we envision expanding SmartTextAI with more advanced natural language processing capabilities. This includes incorporating machine learning models for enhanced keyword extraction, refining spelling correction algorithms, and implementing more sophisticated approaches to next word prediction. We plan to make the tool more user-friendly, possibly developing a graphical interface to cater to a broader audience. Additionally, exploring multilingual support and integrating with external APIs for comprehensive language processing is on our roadmap. SmartTextAI aims to continually evolve, providing users with a powerful and intuitive solution for various text-related tasks.

Built With

• nltk
• python
• rake