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NLP - Applications
AI in NLP: From Word2Vec to Modern Language Models Hosted by Nathan Rigoni
Artificial intelligence has turned the simple act of “reading a document” into a powerful engine for insight—but how did we get from counting words to building whole knowledge bases? What if you could ask a computer to “find every contract clause that mentions liability” and get an instant, accurate list without scrolling through pages of text?
What you will learn
- How early NLP models like Word2Vec turned tokenized words into vectors that capture meaning.
- The linguistic principles behind tokenization and context‑based learning (blank‑out‑word training).
- Why hidden‑state representations enable tasks such as topic analysis and document classification.
- The role of assumed context in information theory (Shannon) and how it shapes modern large language models.
- Common pitfalls such as hallucination and why being explicit with prompts improves results.
Resources mentioned
- Word2Vec (original word‑embedding model).
- Claude Shannon’s Information Theory (basic concepts of bits, messages, and context).
- Ludwig Wittgenstein’s philosophical work on meaning and context.
- Retrieval‑Augmented Generation (RAG) as a technique for grounding LLM responses.
Why this episode matters
Understanding the lineage from Word2Vec to today’s LLMs gives you the toolkit to automate document processing, build smarter classifiers, and avoid the “black‑box” traps that lead to hallucinations. By mastering tokenization and context, you can turn raw text into actionable data, a decisive advantage for any business or researcher navigating today’s information overload.
Subscribe for more deep dives, visit www.phronesis-analytics.com, or email [email protected].
Keywords: NLP, Word2Vec, tokenization, word embeddings, hidden state, auto‑encoder, linguistics, Claude Shannon, information theory, context, assumed context, large language models, hallucination, retrieval‑augmented generation, RAG.
Ludwig Wittgenstien: https://en.wikipedia.org/wiki/Ludwig_Wittgenstein
Claude Shannon - Information Theory: https://en.wikipedia.org/wiki/Information_theory
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By Nathan RigoniAI in NLP: From Word2Vec to Modern Language Models Hosted by Nathan Rigoni
Artificial intelligence has turned the simple act of “reading a document” into a powerful engine for insight—but how did we get from counting words to building whole knowledge bases? What if you could ask a computer to “find every contract clause that mentions liability” and get an instant, accurate list without scrolling through pages of text?
What you will learn
- How early NLP models like Word2Vec turned tokenized words into vectors that capture meaning.
- The linguistic principles behind tokenization and context‑based learning (blank‑out‑word training).
- Why hidden‑state representations enable tasks such as topic analysis and document classification.
- The role of assumed context in information theory (Shannon) and how it shapes modern large language models.
- Common pitfalls such as hallucination and why being explicit with prompts improves results.
Resources mentioned
- Word2Vec (original word‑embedding model).
- Claude Shannon’s Information Theory (basic concepts of bits, messages, and context).
- Ludwig Wittgenstein’s philosophical work on meaning and context.
- Retrieval‑Augmented Generation (RAG) as a technique for grounding LLM responses.
Why this episode matters
Understanding the lineage from Word2Vec to today’s LLMs gives you the toolkit to automate document processing, build smarter classifiers, and avoid the “black‑box” traps that lead to hallucinations. By mastering tokenization and context, you can turn raw text into actionable data, a decisive advantage for any business or researcher navigating today’s information overload.
Subscribe for more deep dives, visit www.phronesis-analytics.com, or email [email protected].
Keywords: NLP, Word2Vec, tokenization, word embeddings, hidden state, auto‑encoder, linguistics, Claude Shannon, information theory, context, assumed context, large language models, hallucination, retrieval‑augmented generation, RAG.
Ludwig Wittgenstien: https://en.wikipedia.org/wiki/Ludwig_Wittgenstein
Claude Shannon - Information Theory: https://en.wikipedia.org/wiki/Information_theory