About This Episode
Today we’re joined by Yunyao Li, a senior research manager at IBM Research.
Yunyao is in a somewhat unique position at IBM, addressing the challenges of enterprise NLP in a traditional research environment, while also having customer engagement responsibilities. In our conversation with Yunyao, we explore the challenges associated with productizing NLP in the enterprise, and if she focuses on solving these problems independent of one another, or through a more unified approach.
We then ground the conversation with real-world examples of these enterprise challenges, including enabling level document discovery at scale using combinations of techniques like deep neural networks and supervised and/or unsupervised learning, and entity extraction and semantic parsing to identify text. Finally, we talk through data augmentation in the context of NLP, and how we enable the humans in-the-loop to generate high-quality data.
Watch on Youtube
Connect with Yunyao!
- Advancing NLP with Project Debater, w/ Noam Slonim – #495
- AutoML for Natural Language Processing with Abhishek Thakur – #475
- Blog: Role of AI in Enterprise Applications – ACM SIGMOD Blog
- Slide show: Taming Wild West of Natural Language Processing
- Course: Tutorial on Table Extraction and Understanding for Scientific and Enterprise Applications – IBM
- Paper: Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context
- Blog: Bringing IBM NLP capabilities to the CORD-19 Dataset
- Paper: TableLab: An Interactive Table Extraction System with Adaptive Deep Learning
- Blog: Deep Document Understanding: IBM’s AI extracts data from complex documents