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6 Best Practices for NLP Implementation

 

Recent advances in artificial intelligence and machine learning have made natural language processing so powerful that state-of-the-art models have surpassed human performance in existing benchmark datasets.

In the education space, we’ve seen NLP used in several powerful ways, from automated translation and helping students improve their writing skills, to enhancing learning experiences. For example, Google Translate helps make educational content useful for more students around the world. Duolingo uses AI to determine the difficulty of language learning content. Grammarly helps students with mistake-free writing, and TurnItIn helps teachers detect plagiarism. At Quizlet, we leverage ML and NLP for
grading written answers, generating questions, and understanding our content, among others.

Having spent the majority of my career applying (or leading teams to apply) ML and NLP to solve problems for users and businesses, here are some guidelines that I recommend keeping in mind when approaching NLP projects.

  1. Know your problem:
    For beginners starting a machine learning problem, it’s easy to get lost in the theory and code. Make sure you understand the problem and hypotheses well NLP has the power to help enhance a user experience and to create new features previously not possible. There are many courses and technical resources to help you learn the technology and tooling, and these steps will help you utilize them in real world settings.

     

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