#062 - Dr. Guy Emerson - Linguistics, Distributional Semantics
Machine Learning Street Talk (MLST)
00:00
Contrasting Approaches in NLP Semantics
This chapter explores the differences between bottom-up and top-down methodologies in natural language processing, emphasizing the need to evaluate both for effective language modeling. It examines language acquisition theories, particularly the poverty of the stimulus argument, and how these can be empirically tested using modern machine learning techniques. The discussion highlights the inherent challenges in capturing the complexities of human language through neural networks while considering the implications of linguistic richness and evolution.
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