Symbolic and Sub-Symbolic Natural Language Processing with Jonathan Mugan - TWiML Talk #49
Analysis
This article summarizes a podcast interview with Jonathan Mugan, CEO of Deep Grammar, focusing on Natural Language Processing (NLP). The interview explores both sub-symbolic and symbolic approaches to NLP, contrasting them with the previous week's interview. It highlights the use of deep learning in grammar checking and discusses topics like attention mechanisms (sequence to sequence) and ontological approaches (WordNet, synsets, FrameNet, SUMO). The article serves as a brief overview of the interview's content, providing context and key topics covered.
Key Takeaways
- •The interview discusses both symbolic and sub-symbolic approaches to NLP.
- •Jonathan Mugan's company, Deep Grammar, uses deep learning for grammar checking.
- •The interview covers topics like attention mechanisms and ontological approaches in NLP.
Reference
“This interview is a great complement to my conversation with Bruno, and we cover a variety of topics from both the sub-symbolic and symbolic schools of NLP...”