US – Google just missed it. These things aren't that complicated, when you look at them one at a time, for Google to fix. The current limitations result from the complexity of the language and the amount of machine learning needed to address it. The approach to addressing this requires building larger and larger sets of examples like the two I shared above, and then using them to help train better algorithms derived from machine learning. RankBrain was a major breakthrough for Google, but the work is in progress. The company is investing heavily to dramatically advance its understanding of the language.
The following excerpt, from USA Today, begins with a quote from Google's senior program manager Linne Ha, who leads the company's team of Pygmalion linguists: “We come up with rules and exceptions for training the computer,” Ha says. "Why do we say 'the jewelry retouching service President of the United States'? And why don't we say "the president of France"? There are all sorts of inconsistencies in our language and in every language. For humans, this seems obvious and natural, but for machines, it's actually quite difficult. Google's Pygmalion team is the one focused on improving Google's understanding of natural language.
Some of the things that will improve at the same time are their understanding of: which pages on the web best match the user's intent as implied by the query. how well a page meets the needs of the user. By doing so, their abilities to measure the quality of content and its ability to meet user intent will increase, which will therefore become an increasingly important ranking factor over time. User engagement / satisfaction As stated earlier, we know that search engines use a variety of methods to measure user engagement.