Philip Mackenzie is a master student at Nanjing University. Philip’s research involves applying NLP techniques to automate systematic literature reviews of academic texts.
Giuseppe Tomasello works in engineering, international management and NLP for models human language
Topic and Audience
Following the last session, this time we are going to dig deeper into the intersection of machine learning and human language learning. How can new tech in AI be used to solve real life human language learning problems?
This session focus on connecting the language learning experts from education companies with technology experts working on solutions based on Natural Language Processing.
Short Introduction for speakers:
Tiffany Wingert will look at the learners’ perspective and addressing some of the following questions: What motivates learners in general? Why do learners choose to use language learning apps and other programs? What are learners’ behaviors regarding apps?
Maijaliisa Mickols will give a primer on some of the basic tenants of second language acquisition and pedagogical approaches for language learning. We’ll also cover how language learning benefits from a holistic approach, and how many existing language learning apps fail to address the range of learner needs.
Gregory Orton will analyze the reasons why adults seek technological solutions for language learning and using this as jumping off point for an exploration of future directions in Ed-tech.
Philip Mackenzie will introduce data preprocessing, the first step in the NLP pipeline and explain why this first step is so important and show some tools that can help us simplify this process.
Giuseppe Tomasello will show some of the most powerful, yet easy to use, NLP (Natural Language Processing) libraries. The objective is to give to the audience an idea about what this technology is capable of doing and what practical application can be implemented to facilitate the human learning process.
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