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Applied Natural Language Understanding: Topic modeling for text insights - Going from LDA to using advances in language models

Posted Nov 24, 2022 | Views 862

Understanding the contents of a large corpus of text, like customer reviews or tweets, is a frequently encountered challenge and a significant ML research undertaking. One approach to tackling this problem is topic modeling. We experience it daily - from trending topics on Twitter to key topics in Amazon's product reviews. In this talk, Prem presents a survey of influential methods like LDA and NMF. He then compares them to recent advances in large language models that began with transformers and showcases techniques to apply them, their effectiveness in tackling this problem, and the challenges that come with them. Prem Viswanathan is a member of Tribe AI, a collective of 250+ engineers and data scientists from industry leaders in AI. He’s also the the Co-Founder & CTO of SwiftCX - an intelligent automation platform for customer teams. Prem also serves as an adjunct faculty at Carnegie Mellon University, Pittsburgh. Previously, he was the Director of ML at, a SaaS startup, where he led applied ML research and engineering in NLP. His research focus is on NLP/NLU and Knowledge Graphs. Prem has over ten years of experience in Machine Learning and building ML-infused products at IBM and AWS. More recently, at AWS, he worked alongside Marinus AI, an AI startup that tackles and disrupts human trafficking, to develop a knowledge graph-powered solution to assist and speed up missing person investigations by 4x.

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