#062 - Dr. Guy Emerson - Linguistics, Distributional Semantics
Machine Learning Street Talk (MLST)
00:00
Complexities of Semantics in Machine Learning
This chapter examines the intersection of distributional and truth conditional semantics, focusing on the historical influences and challenges in capturing meaning, particularly polysemy. It critiques modern linguistic assumptions and discusses the limitations of traditional vector models in representing the complexities of language. The conversation highlights the importance of grounding linguistic understanding in real-world contexts and explores the interplay between large models and human-like language learning methods.
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