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Information Retrieval with Term Frequency and TF-IDF Models

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Photo by Markus Winkler on Unsplash

This article originally appeared on Lemmalytica— a blog about language, artificial intelligence, and coding.

One of the core tasks in information retrieval is searching. Anyone who deals with large amounts of text data (and that’s almost all of us) knows how difficult this seemingly simple task can be. If your search term is too broad, you may find yourself sifting through an impossible quantity of documents. And if your search term is too narrow, you could be missing out on relevant results. So how do we decide which documents are the most relevant to our search?

Search relevance is a difficult problem — and modern search engines employ highly sophisticated (and proprietary) algorithms to deal with the issue. We won’t delve into those algorithms, but let’s look at some simple strategies that you might employ in your own information retrieval applications. …



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Severin Perez

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