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Saturday, November 17 • 3:30pm - 3:50pm
Something for Nothing: Boostrapping Text Classification

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The hardest part of building a text classifier is finding labelled data to train the model. The next hardest part is making sure that data is fair and representative. In this talk we will discuss some approaches to rapidly generating corpora suitable for supervised training from public data and with open-source tools. This talk will include some practical tips as well as some less-obvious pitfalls, and is suitable for both novices and more experienced Natural Language Processing Practitioners. At the end of the talk you will be able to give a convincing answer to the eternal question: how do I build a text classifier for a product that doesn't exist yet?

Speakers
avatar for Alizishaan Khatri

Alizishaan Khatri

Machine Learning Engineer, Pivotus
Alizishaan's professional passions revolve around two things : using technology to solve real-world problems and sharing solutions with the community. He is currently employed as a Machine Learning Engineer with Pivotus where he works on problems in the Natural Language Processing... Read More →
avatar for Alexander O'Connor

Alexander O'Connor

Director of Research / Machine Learning Engineer, Pivotus
Alexander O'Connor is Director of Research with Pivotus, a banking startup bringing human contact back to banking. At Pivotus, Alex is responsible for data wrangling and machine learning. Previously, he worked as a researcher and academic in universities in Dublin Ireland (DCU & Trinity... Read More →


Saturday November 17, 2018 3:30pm - 3:50pm
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