Our own experiences as students motivated us.

What it does

It extracts key terms from an input text file and outputs the key terms, as well as brief descriptions and a related video for each term.

How I built it

With SpaCy, we extracted the key terms using its entity recognition ability. Then, we scraped google search results using BeautifulSoup4.

Challenges I ran into

At first, we wanted to train our own NER model, but it proved too complex to complete in the time we had. Thus, we opted to use SpaCy's NER model/feature instead.

Accomplishments that I'm proud of

We were able to complete our project and create a user interface on a website.

What I learned

How to scrape HTML docs in Python, which definitely boosted my skills and challenged my ability to code.

What's next for StudyAssistant

Train its own NER model specifically using academic texts, and use NLP to write notes based on the input text file.

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