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How AI Is Built  cover image

From Keywords to AI (to GAR): The Evolution of Search, Finding Search Signals | S2 E3

How AI Is Built

NOTE

Embrace Statistical Significance for Effective Phrase Matching

Utilizing statistical significance to identify co-locations in text can enhance phrase matching in natural language processing (NLP). Techniques such as those offered by libraries like Gensim allow for the straightforward discovery of significant phrases without the need for complex extraction systems. Recognizing which terms commonly occur together enables more effective query handling, particularly in the context of language models (LLMs). The evolution of LLMs simplifies the process of extracting relevant entities, reducing the complexity traditionally associated with entity classification and enabling quick adjustments to relevance tuning. Furthermore, the rise of LLMs allows for practical examples and few-shot learning to automate the extraction of key attributes, making it easier to adapt queries and improve content understanding in various applications.

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