From Noise to Knowledge
from Émission de podcast sur le cloud computi Hosted by Marthinusbaloyi
This podcast explores how transformer-based language models and clustering techniques can be used to resolve keyword ambiguity and improve the quality of knowledge graphs. It discusses entity resolution, semantic similarity, text embeddings, unsupervised learning, and methods for transforming noisy data into structured, reliable knowledge. Perfect for AI engineers, NLP practitioners, data scientists, and knowledge graph professionals working with large-scale text and information systems.
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