Four Key Tools for Robust Enterprise NLP with Yunyao Li
Published:Nov 18, 2021 18:29
•1 min read
•Practical AI
Analysis
This article from Practical AI discusses the challenges and solutions for implementing Natural Language Processing (NLP) in enterprise settings. It features an interview with Yunyao Li, a senior research manager at IBM Research, who provides insights into the practical aspects of productizing NLP. The conversation covers document discovery, entity extraction, semantic parsing, and data augmentation, highlighting the importance of a unified approach and human-in-the-loop processes. The article emphasizes real-world examples and the use of techniques like deep neural networks and supervised/unsupervised learning to address enterprise NLP challenges.
Key Takeaways
- •Enterprise NLP faces unique challenges in productization.
- •Unified approaches are preferred over isolated solutions.
- •Data augmentation and human-in-the-loop are crucial for high-quality data.
Reference
“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.”