How NLP Evolved: From Word Counts to Transformers with Francesco Fabozzi, PhD
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About this episode
Francesco Fabozzi, PhD, Managing Editor of the Journal of Financial Data Science, joins Lotta Moberg, CFA, PhD to unpack how natural language processing matured into the powerful tool it is today. The discussion traces early finance‑focused techniques—dictionary counts, sentiment word lists, and sparse document‑term matrices, along with their limits around context and negation. Fabozzi then explains how neural networks introduced embeddings and contextual meaning, paving the way for recurrent models and eventually transformer architectures. He breaks down how self‑attention, encoder–decoder designs, and decoder‑only LLMs transformed language understanding and made large‑scale modeling feasible.
This episode lays the groundwork for understanding how modern NLP models interpret financial text. Look for Part 2, where the conversation turns to practical applications in investment management.
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