A key application of query rewriting is increasing recall — that is, matching a larger set of relevant results. In extreme cases, increasing recall means the difference between returning some results and returning no results. More typically, query expansion casts a wider net for results that are…

The previous two posts focused on using query rewriting to increase recall. We can also use query rewriting to increase precision — that is, to reduce the number of irrelevant results. While increasing recall helps us avoid small or empty result sets, increasing precision helps us avoid large…

Faceted search is a topic broad enough to deserve its own book. It has become a standard feature of all modern search engines, including open-source platforms like Solr and Elastic.

In this post, I’ll quickly explain how faceted classification and faceted search work. I’ll…

Now that we can handle characters, let’s move on to words.

A critical task for query and document understanding is breaking up the text into a sequence of words. We call these words tokens — but as we’ll see in a moment, tokens include strings that aren’t necessarily words that…

In the previous post, we discussed query expansion as a way to increase recall. In this post we’ll discuss the other major technique for increasing recall: query relaxation.

Query relaxation feels like the opposite of query expansion. Instead of adding tokens to the query…

In the previous post, we discussed how query segmentation improves precision by grouping query words into semantic units. In this post, we’ll discuss query scoping, another query rewriting technique that improves precision by matching each query segment to the right attribute.