Google Pros&Cons are not Rich Results but Annotations

The SEO industry is so littered with myths that sometimes a little learning could be a dangerous thing.

One of them is that you can buy SEO happiness with structured data, meaning that you can embellish the likes of your web page on the SERPs by calling up all the schema types you want. To play devil's advocate, what if structured data were just one step from being dismissed forever?

Google recently tested a feature called Pros & Cons right below the meta description's callout on the SERP.

Despite being very similar to rich results, these features represent a fair example of how Google kickstarted the parsing of plain text from a content copy on a page. In other words, this is one of the earliest excerpts of unstructured data retrieved by the search engine being showcased alongside structured data on the SERP.

Hey hold your horses!?? (I can hear you ). Apologies for jumping the gun ????.

In this post, I am going to talk you through the so-called "Annotations" providing an overview of their main features and the hows behind their organic generation on the search results page.

If you want to learn more about Annotations, I do suggest you take a deep dive into the spectacular blog post covered by Marie Haynes about how to use annotations to create better content

What are Annotations?

Following some research, I came across a Google patent called Search result annotations , covering all the bits and pieces showing up on the SERPs that have nothing to do with the traditional blue links. Among them, there were of course the bespoke annotations.

In a nutshell, Annotations refer to HTML strings retrieved from unstructured data sources, thus plain text from a content copy with the likes of either a product page or a landing page.

How are Annotations being generated?

Annotations are extracted from multiple text-based sources on a webpage.

The patent provides a scientific and exhaustive explanation of the process. Despite the high level of confidence stemming from the reliability of the model, Google patents oftentimes aren't being executed verbatim. Hence, you always want to take them with a solid grain of salt.

In short, the query engine detects an annotation from a page and determines whether to process it in real-time?or store it in the search index. Next, a supervised machine learning model scores the annotations by type and ultimately ranks them by usefulness.

To expand a bit on the context, let's consider the previous example about Larceny Bourbon.

Recent progress in the NLP machine learning model has enabled Google to parse unstructured data, that is human-readable text. As we can see from the screen grab below, the yellowed words are those transferred to the SERP as annotations.

This boils down to progress in advanced cluster analysis tasks carried out on entities and primarily n-grams. In fact, from the screenshot, it's clear that the bespoke annotations stem from a sequence of characters (n-grams) completing the definition of specific entities.

Let’s take one specific source of annotations, “smooth and tasty bourbon”.

First, let’s tokenize the n-gram:

bourbon = entity

smooth = connector type

tasty = connector type

and = proposition
        

Now we can raise an assumption about how Google addresses the n-gram as entities.

“tasty bourbon” = Entity

“smooth bourbon” = Entity        

In brief, the web page emphasizes the root entity “bourbon” with the enforcement yielded from the connectors “tasty” and “smooth”.?

Google is reinforcing relationships among entities on the Internet following advancements in unsupervised cluster analysis tasks run by machine learning models.

Annotations Main Features

These HTML strings come up with a few remarkable traits which could potentially disclose rooms for actions or improvement on one's page content.

• Annotations tend to be query dependant, meaning they show up depending on the type of search query. As inferred by the patent, fat-head terms are the most prone to conjure up these features.
• Annotations tend to show up depending on the viewport size. Depending on the size of your device, whether it being a desktop or a mobile phone or else, annotations will be more or less keen to pop up.
• Annotations can help Google interpret entities from the search queries. Being usually fetched as n-grams, annotations may provide pivotal hints to Google about reinforcing entity relationship.
• Annotations can help reduce pogo-sticking on the SERP, as they are better off at matching search intent. This means that they can potentially entice an increase in CTR.

In layer man's terms, annotations play a two-folded role resulting beneficial to both the search engine and the public. While resulting helpful to users by moving the needle of a shallow search intent, they provide Google with hint on how to improve the search engine entity network.

Example of Annotations

Whether the SEO industry came up with a Pros&Cons excerpts of this feature, annotations are actually available in different tastes and flavours.

To stick with the findings provided by the patent, annotations can fit the shoes of List Includes, Version change, Media Annotations or Editorial Reviews (see image below)

In the words of the patent, the above example of editorial reviews is:

“An annotation including a snippet of a user review that mentions running in conjunction with headphones. That users mention running in reviews for a product?may result in a higher ranking for the particular product.”

However, there are a few other examples falling under the surface that Google officially doesn't recognize. In fact, the patent may have missed out on the HTML table snippets perhaps due to a blunder or most likely to the mere flow of time from the patent's release.

Conclusion

Rolling back on the Pros&Cons SERP feature, you might be well-equipped to infer that they represent HTML strings retrieved from pages with multiple reviews available, likely clustered by n-grams.

The search engine is making progress in parsing not only human-friendly written structured data but also unstructured data or plain text on a page.

The easier to understand the content on a page, the easier for Google to whizz it through its unsupervised cluster analysis models to generate entities, thereby displaying helpful annotations on the SERP.