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Monthly Archives: November 2020

Smart Shopping for the Holidays – A Test & Learn Approach

November 30, 2020 No Comments

Should eCommerce advertisers test Google’s Smart Shopping campaigns? One strategist breaks down a recent test and several tips to boost results.

Read more at PPCHero.com
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Seven most popular types of blog posts guaranteed to boost traffic

November 30, 2020 No Comments

30-second summary:

  • Optimizing your content for search results requires search intent.
  • Understanding search intent will help you generate effective content.
  • Target search intent by examining high ranking search results.
  • How-to and listicles are the most shared blog post ideas.
  • Focusing on key on-page SEO elements drives higher search visibility.

When it comes to blog posts, not all content formats are created equal.

What’s more, with more than 500 million blogs out there all vying for attention, it’s getting harder and harder to stand out amidst the noise. 

For a blog to be successful these days, it takes more than just shareable images or enticing headlines. While these elements are undoubtedly important, writing blog posts that attract the right kind of reader requires careful ideation, optimization, and outreach.

Fortunately, SEO content is not rocket science. Whether you’re struggling with content ideas or looking to monetize your ideas better, below are the seven most popular types of posts that will help your blog gain better traction and drive traffic to your site.

Why understanding search intent matters

Before I identify the blog post types already proven to deliver results, we must first talk about search intent. If you don’t know what search intent is, search intent is the why behind a specific search.   

Each type of search falls into one (or several) intention types:

1. Informational intent

The search user wants to learn something. While this type of search typically includes words like “how-to,” “what is,” or “who,” not all informational searches are posed as questions (for example, JFK International Airport directions).

2. Navigational intent

The search user wants to visit a specific site. People would rather ask a search engine than type the full web address in the URL bar because they may be unsure of the exact website. Examples include “Facebook” or “WestIn contact number.”

3. Transactional intent

The search user wants to purchase something. A transactional intent typically means the search user is wallet-ready. They’re merely looking for a website to make a purchase. Typical search queries include “buy iPhone 12,” “spa package,” and “MacBook air cheap.”

4. Commercial investigation

The search user has the intention to buy but is still at the research stage. People performing these types of searches require more information about the product or service that they have an interest in buying. 

They search for terms like “top restaurant in New York” or “best android phone” to compare a specific product or service.

By understanding the specific intent behind a search, you can optimize your blog post for the right search terms. And when the correct type of searcher finds your content, your blog can generate relevant and targeted traffic.

How to target search intent with your blog 

With Google’s perpetual goal focused on providing the most relevant information for a search query, aligning your content with your audience’s search intent allows your blog to rank high for relevant search results.

For SEO success, relevance is a core tenet not to be overlooked.

So how can you infer search intent and create content that drives significant traffic potential?

The answer lies in the search query itself.

Let’s look at the search term “how to bake a cake,” for example. For those keywords alone, it may appear like the search has informational intent. But, don’t just guess search intent. A quick way to confirm the specific intent of a search is by performing a Google search.

By inputting your keywords into Google’s search, which in this case is “how to bake a cake,” it’s clear from the results that users are looking for cake recipe ideas and baking guides. To rank competitively high for this type of search intent, you should focus your content around a how-to post or a list article. 

Now that we have a better grasp of search intent and its role in content creation, let’s look at the most popular blog post ideas that you can use today to start producing high-quality content.

Seven blog post ideas that deliver valuable, engaging content

Ready to put virtual pen to paper? Take the guesswork out of content ideation with these top-ranked content ideas.

1. How-tos and tutorials

With 80% of all Google searches being informational, how-to and tutorial posts are a staple for any blog, no matter your niche or industry. Since the goal of a how-to guide or tutorial is to solve a problem, readers of your article will be more inclined to invest in your product or service.

And as you’re an authority in your business, how-to type articles are simple ways to connect with your audience and establish credibility while showcasing your expertise.

To maximize the effectiveness of these types of post ideas, be sure to include visuals like images and videos in your articles. Not only do visuals enrich the content, but they also help readers comprehend the information provided better.

Readers are also more willing to take action when content is easy to process.

A great example of how-to content is Ann Smarty’s blog post, “Google’s featured snippets: How to get your YouTube video featured in Google.” 

2. Listicles

Another content powerhouse, list articles (listicles) help to streamline information. Just how powerful are list articles? 

In a comparison between list-based articles and non-list articles, SEMrush found that the presence of lists resulted in 4x more traffic and 2x more social shares. What’s more, 36% of readers are more likely to click on an article with list headlines. 

With content typically formatted as a numbered list, readers can quickly consume the content of your list posts. It being easily digestible also helps readers better share the post and act on the information.

Like how-to guides, list-style articles can be a useful tool for informational intent, as well as transactional intent and commercial investigation.

As an example, here’s an excellent list-based post on web development tools

3. Case studies

A case study post is a highly valuable marketing and brand promotional tool. In B2B marketing, case studies can provide the following five benefits:

  • Showcase the tangible value of your product and capabilities
  • Highlights how your product resolves customer pain points
  • Establish credibility with real customers
  • Provide social proof for prospective customers
  • Uncover your brand evangelists 

In several content marketing surveys, B2B marketers identified customer testimonials (89%) and case studies (88%) as being the most effective content marketing tools for lead generation. And three-quarters of B2B marketers found case studies accelerated leads through latter stages of the funnel quicker than any other content marketing format. 

To realize the power of case studies, structure your process from challenge or problem to potential solutions and, finally, the results and conclusion. Here’s a great SEO case study example showing off this structure without being dull or boring.

4. Predictions and trends

The brilliance of writing posts on future trends is that you’re able to display your expertise and industry knowledge. What’s more, as people are always looking for advice or information about the next market trend (commercial investigation), prediction posts can generate great responses, and even spark debates.

Statistics by Hubspot found that few people who regularly read blogs do so to learn about a brand’s products. Instead, people commonly read blogs for three reasons:

  • To learn something new
  • To be entertained
  • To learn about news or trends in their industry

And when it comes to content formats, 47% of bloggers have found trend pieces to be highly popular among their readers. Prediction and trend post ideas are only outpaced by lists (57%) and how-to articles (77%). 

5. Ultimate guides 

Ultimate guides are the most definitive blog posts you can write. These types of long-form post ideas typically exceed 3,000 words. Some guides can even take as many as 10,000+ words to write effectively. 

So why would you want to commit to writing a detailed, comprehensive blog post? Here are a few benefits to ultimate guides:

  • Produce evergreen content that produces traffic year-round
  • Positions you and your brand as a subject matter expert
  • Indicator of relevance, which is vital to search intent
  • Provides your brand with marketing campaign assets
  • Receives more social shares, increasing content engagement
  • Expands keyword opportunities

Regardless of the topic or niche, long-form content outperforms shorter blog posts. In a study done by Brian Dean, blog posts longer than 3,000 words had 77.2% more referring domains than short-form content. And thanks to Google’s RankBrain, long-form content gets rewarded with higher-ranking positions. 

Bloggers who work on long reads experience 54% better results and receive 3x more traffic than blogs who only write up short content.

6. Interviews

Interview posts are a great addition to any blog as it diversifies your site’s blog content and relieves some pressure to content creation. Interviews allow your brand to:

  • Expand its influence
  • Broaden its network
  • Generate more quality backlinks
  • Increase its authority
  • Diversify its blog content

As an influencer outreach tool, interviews are undeniably powerful. With 69% of consumers distrusting traditional advertising, collaborative content like interviews enables your brand to reach and connect with audiences in a more natural way.   

First Round Capital, a seed-stage venture firm, knows all too well the transformative power of interviews. A single interview about Slack’s launch strategy earned First Round Capital a total of 2,243 backlinks from major publication sites like Fast Company and Entrepreneur.

If your blog is relatively new and you’re unable to attract any influencers to interview, consider writing expert round-up posts. Influencers love participating in round-up posts as these provide them with opportunities to demonstrate their expertise.

Both post ideas can contribute to more significant blog traffic as influencers are more willing to share your content with their network.

7. Infographics

As images are more attention-grabbing than text, consider adding infographics to your blog content calendar. Infographics are not just attractive or exciting to read; they are also shared 3x more than any other type of content.

Admittedly, infographics work best when professionally designed. Fortunately, there are many online infographic tools like Canva and Piktochart that enable you to create beautiful infographics at a freemium price.

Now that you have plenty of post ideas to keep your content calendar full, let’s look at specific on-page SEO elements that will help attract the right visitors to your blog.

Three on-page SEO factors for greater searchability

Whether you’re looking to write a listicle, tutorial, or ultimate guide, include these three on-page SEO factors into your content before hitting publish.

1. Target one or two medium-tail keywords

As the primary goal for any blog is to attract an audience, keyword research is vital. After all, no content can compete in search results without keyword research. If you don’t know what your audience is searching for, how can you get your content in front of them?

With that said, though, don’t try to rank for short-head search terms like “chocolate cake.” These search terms are highly competitive, making it difficult to rank high against already established blogs. Instead, focus on medium-tail keywords like “chocolate pound cake recipe.”

Medium-tail keywords, like the previous example, are more specific than short-head terms. People using medium-tail keywords are more likely to read your content. They are also more motivated to take action, resulting in a positive interaction with your brand.

Once you’ve done your keyword research and compiled a list of medium-tail keywords, include them into these important places in your blog post:

  • Title tag
  • Headers
  • URL
  • Meta description

You can also add your keywords into the body but don’t over-optimize your content. 

Over-optimization is a form of keyword stuffing, which goes against Google’s guidelines. Just add your target keyword in the first 100 words of your article.

2. Link to important pages

Internal links are hyperlinks that point to a different web page on the same domain. Internal links are an SEO best practice because it helps search engines find and index relevant content. Visitors also use internal links to check out high-value pages, increasing site dwell time.

When linking internally, aim for two to three links. Use a descriptive anchor text with keywords that are relevant to the linked-to page. Another way to include more internal links to your blog post is by adding a related post section at the bottom of the page.

3. Optimize images for maximum shareability

Blog posts that only contain text are flat-out dull. Adding quality images to your post better explains complex information and makes your content more visually appealing. Because visuals stand out, images can improve the scannability of your post significantly.

But don’t just pop images into your post and hit publish. Images can also be optimized for SEO, allowing your visuals to rank for Google Images. When optimizing images for search, be sure to:

  • Write a descriptive alt text with your keyword.
  • Keep alt text under 125 characters.
  • Include your target keyword in the filename.
  • Compress the image for faster load times.
  • Use unique images rather than stock imagery.
  • Use the proper file extension for your image.
  • Resize your image to optimum proportions.

Putting it all together

By writing for relevant search intent and incorporating these SEO best practices to your post ideas, your blog will gain more opportunities to appear high in search results. More visibility in search means increased organic traffic to your blog.

After you’ve published your blog post, let the world know about it. Promoting your content via outreach can be done by sharing your post on social media, engaging in forums like Reddit, reaching out to influencers, and advertising through Facebook.

Karl Tablante is Inbound Marketing Manager at SEO Sherpa.

The post Seven most popular types of blog posts guaranteed to boost traffic appeared first on Search Engine Watch.

Search Engine Watch


IBM is acquiring APM startup Instana as it continues to expand hybrid cloud vision

November 30, 2020 No Comments

As IBM transitions from software and services to a company fully focussed on hybrid cloud management, it announced  its intention to buy Instana, an applications performance management startup with a cloud native approach that fits firmly within that strategy.

The companies did not reveal the purchase price.

With Instana, IBM can build on its internal management tools, giving it a way to monitor containerized environments running Kubernetes. It hopes by adding the startup to the fold it can give customers a way to manage complex hybrid and multi-cloud environments.

“Our clients today are faced with managing a complex technology landscape filled with mission-critical applications and data that are running across a variety of hybrid cloud environments – from public clouds, private clouds and on-premises,” Rob Thomas, senior vice president for cloud and data platform said in a statement. He believes Instana will help ease that load, while using machine learning to provide deeper insights.

At the time of the company’s $ 30 million Series C in 2018, TechCrunch’s Frederic Lardinois described the company this way. “What really makes Instana stand out is its ability to automatically discover and monitor the ever-changing infrastructure that makes up a modern application, especially when it comes to running containerized microservices.” That would seem to be precisely the type of solution that IBM would be looking for.

As for Instana, the founders see a good fit for the two companies, especially in light of the Red Hat acquisition in 2018 that is core to IBM’s hybrid approach. “The combination of Instana’s next generation APM and Observability platform with IBM’s Hybrid Cloud and AI technologies excited me from the day IBM approached us with the idea of joining forces and combining our technologies,” CEO Mirko Novakovic wrote in a blog post announcing the deal.

Indeed, in a recent interview IBM CEO Arvind Krishna told CNBC’s Jon Fortt, that they are betting the farm on hybrid cloud management with Red Hat at the center. When you combine that with the decision to spin out the company’s managed infrastructure services business, this purchase shows that they intend to pursue every angle

“The Red Hat acquisition gave us the technology base on which to build a hybrid cloud technology platform based on open-source, and based on giving choice to our clients as they embark on this journey. With the success of that acquisition now giving us the fuel, we can then take the next step, and the larger step, of taking the managed infrastructure services out. So the rest of the company can be absolutely focused on hybrid cloud and artificial intelligence,” Krishna told CNBC.

Instana, which is based in Chicago with offices in Munich, was founded in 2015 in the early days of Kubernetes and the startup’s APM solution has evolved to focus more on the needs of monitoring in a cloud native environment. The company raised $ 57 million along the way with the most recent round being that Series C in 2018.

The deal per usual is subject to regulatory approvals, but the company believes it should close in the next few months.


Enterprise – TechCrunch


The Absolute Best Cyber Monday Deals Online

November 30, 2020 No Comments

Here are the very best discounts we’ve found in every category and at all the major retailers.
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Adjusting Featured Snippet Answers by Context

November 29, 2020 No Comments

How Are Featured Snippet Answers Decided Upon?

I recently wrote about Featured Snippet Answer Scores Ranking Signals. In that post, I described how Google was likely using query dependent and query independent ranking signals to create answer scores for queries that were looking like they wanted answers.

One of the inventors of that patent from that post was Steven Baker. I looked at other patents that he had written, and noticed that one of those was about context as part of query independent ranking signals for answers.

Remembering that patent about question-answering and context, I felt it was worth reviewing that patent and writing about it.

This patent is about processing question queries that want textual answers and how those answers may be decided upon.

it is a complicated patent, and at one point the description behind it seems to get a bit murky, but I wrote about when that happened in the patent, and I think the other details provide a lot of insight into how Google is scoring featured snippet answers. There is an additional related patent that I will be following up with after this post, and I will link to it from here as well.

This patent starts by telling us that a search system can identify resources in response to queries submitted by users and provide information about the resources in a manner that is useful to the users.

How Context Scoring Adjustments for Featured Snippet Answers Works

Users of search systems are often searching for an answer to a specific question, rather than a listing of resources, like in this drawing from the patent, showing featured snippet answers:

featured snippet answers

For example, users may want to know what the weather is in a particular location, a current quote for a stock, the capital of a state, etc.

When queries that are in the form of a question are received, some search engines may perform specialized search operations in response to the question format of the query.

For example, some search engines may provide information responsive to such queries in the form of an “answer,” such as information provided in the form of a “one box” to a question, which is often a featured snippet answer.

Some question queries are better served by explanatory answers, which are also referred to as “long answers” or “answer passages.”

For example, for the question query [why is the sky blue], an answer explaining light as waves is helpful.

featured snippet answers - why is the sky blue

Such answer passages can be selected from resources that include text, such as paragraphs, that are relevant to the question and the answer.

Sections of the text are scored, and the section with the best score is selected as an answer.

In general, the patent tells us about one aspect of what it covers in the following process:

  • Receiving a query that is a question query seeking an answer response
  • Receiving candidate answer passages, each passage made of text selected from a text section subordinate to a heading on a resource, with a corresponding answer score
  • Determining a hierarchy of headings on a page, with two or more heading levels hierarchically arranged in parent-child relationships, where each heading level has one or more headings, a subheading of a respective heading is a child heading in a parent-child relationship and the respective heading is a parent heading in that relationship, and the heading hierarchy includes a root level corresponding to a root heading (for each candidate answer passage)
  • Determining a heading vector describing a path in the hierarchy of headings from the root heading to the respective heading to which the candidate answer passage is subordinate, determining a context score based, at least in part, on the heading vector, adjusting the answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score
  • Selecting an answer passage from the candidate answer passages based on the adjusted answer scores

Advantages of the process in the patent

  1. Long query answers can be selected, based partially on context signals indicating answers relevant to a question
  2. The context signals may be, in part, query-independent (i.e., scored independently of their relatedness to terms of the query
  3. This part of the scoring process considers the context of the document (“resource”) in which the answer text is located, accounting for relevancy signals that may not otherwise be accounted for during query-dependent scoring
  4. Following this approach, long answers that are more likely to satisfy a searcher’s informational need are more likely to appear as answers

This patent can be found at:

Context scoring adjustments for answer passages
Inventors: Nitin Gupta, Srinivasan Venkatachary , Lingkun Chu, and Steven D. Baker
US Patent: 9,959,315
Granted: May 1, 2018
Appl. No.: 14/169,960
Filed: January 31, 2014

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for context scoring adjustments for candidate answer passages.

In one aspect, a method includes scoring candidate answer passages. For each candidate answer passage, the system determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading to which the candidate answer passage is subordinate; determines a context score based, at least in part, on the heading vector; and adjusts answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score.

The system then selects an answer passage from the candidate answer passages based on the adjusted answer scores.

Using Context Scores to Adjust Answer Scores for Featured Snippets

A drawing from the patent shows different hierarchical headings that may be used to determine the context of answer passages that may be used to adjust answer scores for featured snippets:

Hierarchical headings for featured snippets

I discuss these headings and their hierarchy below. Note that the headings include the Page title as a heading (About the Moon), and the headings within heading elements on the page as well. And those headings give those answers context.

This context scoring process starts with receiving candidate answer passages and a score for each of the passages.

Those candidate answer passages and their respective scores are provided to a search engine that receives a query determined to be a question.

Each of those candidate answer passages is text selected from a text section under a particular heading from a specific resource (page) that has a certain answer score.

For each resource where a candidate answer passage has been selected, a context scoring process determines a heading hierarchy in the resource.

A heading is text or other data corresponding to a particular passage in the resource.

As an example, a heading can be text summarizing a section of text that immediately follows the heading (the heading describes what the text is about that follows it, or is contained within it.)

Headings may be indicated, for example, by specific formatting data, such as heading elements using HTML.

This next section from the patent reminded me of an observation that Cindy Krum of Mobile Moxie has about named anchors on a page, and how Google might index those to answer a question, to lead to an answer or a featured snippet. She wrote about those in What the Heck are Fraggles?

A heading could also be anchor text for an internal link (within the same page) that links to an anchor and corresponding text at some other position on the page.

A heading hierarchy could have two or more heading levels that are hierarchically arranged in parent-child relationships.

The first level, or the root heading, could be the title of the resource.

Each of the heading levels may have one or more headings, and a subheading of a respective heading is a child heading and the respective heading is a parent heading in the parent-child relationship.

For each candidate passage, a context scoring process may determine a context score based, at least in part, on the relationship between the root heading and the respective heading to which the candidate answer passage is subordinate.

The context scoring process could be used to determine the context score and determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading.

The context score could be based, at least in part, on the heading vector.

The context scoring process can then adjust the answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score.

The context scoring process can then select an answer passage from the candidate answer passages based on adjusted answer scores.

This flowchart from the patent shows the context scoring adjustment process:

context scoring adjustment flowchart

Identifying Question Queries And Answer Passages

I’ve written about understanding the context of answer passages. The patent tells us more about question queries and answer passages worth going over in more detail.

Some queries are in the form of a question or an implicit question.

For example, the query [distance of the earth from the moon] is in the form of an implicit question “What is the distance of the earth from the moon?”

An implicit question - the distance from the earth to the moon

Likewise, a question may be specific, as in the query [How far away is the moon].

The search engine includes a query question processor that uses processes that determine if a query is a query question (implicit or specific) and if it is, whether there are answers that are responsive to the question.

The query question processor can use several different algorithms to determine whether a query is a question and whether there are particular answers responsive to the question.

For example, it may use to determine question queries and answers:

  • Language models
  • Machine learned processes
  • Knowledge graphs
  • Grammars
  • Combinations of those

The query question processor may choose candidate answer passages in addition to or instead of answer facts. For example, for the query [how far away is the moon], an answer fact is 238,900 miles. And the search engine may just show that factual information since that is the average distance of the Earth from the moon.

But, the query question processor may choose to identify passages that are to be very relevant to the question query.

These passages are called candidate answer passages.

The answer passages are scored, and one passage is selected based on these scores and provided in response to the query.

An answer passage may be scored, and that score may be adjusted based on a context, which is the point behind this patent.

Often Google will identify several candidate answer passages that could be used as featured snippet answers.

Google may look at the information on the pages where those answers come from to better understand the context of the answers such as the title of the page, and the headings about the content that the answer was found within.

Contextual Scoring Adjustments for Featured Snippet Answers

The query question processor sends to a context scoring processor some candidate answer passages, information about the resources from which each answer passages was from, and a score for each of the featured snippet answers.

The scores of the candidate answer passages could be based on the following considerations:

  • Matching a query term to the text of the candidate answer passage
  • Matching answer terms to the text of the candidate answer passages
  • The quality of the underlying resource from which the candidate answer passage was selected

I recently wrote about featured snippet answer scores, and how a combination of query dependent and query independent scoring signals might be used to generate answer scores for answer passages.

The patent tells us that the query question processor may also take into account other factors when scoring candidate answer passages.

Candidate answer passages can be selected from the text of a particular section of the resource. And the query question processor could choose more than one candidate answer passage from a text section.

We are given the following examples of different answer passages from the same page

(These example answer passages are referred to in a few places in the remainder of the post.)

  • (1) It takes about 27 days (27 days, 7 hours, 43 minutes, and 11.6 seconds) for the Moon to orbit the Earth at its orbital distance
  • (2) Why is the distance changing? The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
  • (3) The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles

Each of those answers could be good ones for Google to use. We are told that:

More than three candidate answers can be selected from the resource, and more than one resource can be processed for candidate answers.

How would Google choose between those three possible answers?

Google might decide based on the number of sentences and a selection of up to a maximum number of characters.

The patent tells us this about choosing between those answers:

Each candidate answer has a corresponding score. For this example, assume that candidate answer passage (2) has the highest score, followed by candidate answer passage (3), and then by candidate answer passage (1). Thus, without the context scoring processor, candidate answer passage (2) would have been provided in the answer box of FIG. 2. However, the context scoring processor takes into account the context of the answer passages and adjusts the scores provided by the query question processor.

So, we see that what might be chosen based on featured snippet answer scores could be adjusted based on the context of that answer from the page that it appears on.

Contextually Scoring Featured Snippet Answers

This process starts which begins with a query determined to be a question query seeking an answer response.

This process next receives candidate answer passages, each candidate answer passage chosen from the text of a resource.

Each of the candidate answer passages are text chosen from a text section that is subordinate to a respective heading (under a heading) in the resource and has a corresponding answer score.

For example, the query question processor provides the candidate answer passages, and their corresponding scores, to the context scoring processor.

A Heading Hierarchy to Determine Context

This process then determines a heading hierarchy from the resource.

The heading hierarchy would have two or more heading levels hierarchically arranged in parent-child relationships (Such as a page title, and an HTML heading element.)

Each heading level has one or more headings.

A subheading of a respective heading is a child heading (an (h2) heading might be a subheading of a (title)) in the parent-child relationship and the respective heading is a parent heading in the relationship.

The heading hierarchy includes a root level corresponding to a root heading.

The context scoring processor can process heading tags in a DOM tree to determine a heading hierarchy.

hierarchical headings for featured snippets

For example, concerning the drawing about the distance to the moon just above, the heading hierarchy for the resource may be:

The ROOT Heading (title) is: About The Moon (310)

The main heading (H1) on the page

H1: The Moon’s Orbit (330)

A secondary heading (h2) on the page:

H2: How long does it take for the Moon to orbit Earth? (334)

Another secondary heading (h2) on the page is:

H2: The distance from the Earth to the Moon (338)

Another Main heading (h1) on the page

H1: The Moon (360)

Another secondary Heading (h2) on the page:

H2: Age of the Moon (364)

Another secondary heading (h2) on the page:

H2: Life on the Moon (368)

Here is how the patent describes this heading hierarchy:

In this heading hierarchy, The title is the root heading at the root level; headings 330 and 360 are child headings of the heading, and are at a first level below the root level; headings 334 and 338 are child headings of the heading 330, and are at a second level that is one level below the first level, and two levels below the root level; and headings 364 and 368 are child headings of the heading 360 and are at a second level that is one level below the first level, and two levels below the root level.

The process from the patent determines a context score based, at least in part, on the relationship between the root heading and the respective heading to which the candidate answer passage is subordinate.

This score may be is based on a heading vector.

The patent says that the process, for each of the candidate answer passages, determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading.

The heading vector would include the text of the headings for the candidate answer passage.

For the example candidate answer passages (1)-(3) above about how long it takes the moon to orbit the earch, the respectively corresponding heading vectors V1, V2 and V3 are:

  • V1=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: How long does it take for the Moon to orbit the Earth?]>
  • V2=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: The distance from the Earth to the Moon]>
  • V3=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: The distance from the Earth to the Moon]>

We are also told that because candidate answer passages (2) and (3) are selected from the same text section 340, their respective heading vectors V2 and V3 are the same (they are both in the content under the same (H2) heading.)

The process of adjusting a score, for each answer passage, uses a context score based, at least in part, on the heading vector (410).

That context score can be a single score used to scale the candidate answer passage score or can be a series of discrete scores/boosts that can be used to adjust the score of the candidate answer passage.

Where things Get Murky in This Patent

There do seem to be several related patents involving featured snippet answers, and this one which targets learning more about answers from their context based on where they fit in a heading hierarchy makes sense.

But, I’m confused by how the patent tells us that one answer based on the context would be adjusted over another one.

The first issue I have is that the answers they are comparing in the same contextual area have some overlap. Here those two are:

  • (2) Why is the distance changing? The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
  • (3) The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles

Note that the second answer and the third answer both include the same line: “Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles.” I find myself a little surprised that the second answer includes a couple of sentences that aren’t in the third answer, and skips a couple of lines from the third answer, and then includes the last sentence, which answers the question.

Since they both appear in the same heading and subheading section of the page they are from, it is difficult to imagine that there is a different adjustment based on context. But, the patent tells us differently:

The candidate answer score with the highest adjusted answer score (based on context from the headings) is selected, and the answer passage.

Recall that in the example above, the candidate answer passage (2) had the highest score, followed by candidate answer passage (3), and then by candidate answer passage (1).

However, after adjustments, candidate answer passage (3) has the highest score, followed by candidate answer passage (2), and then-candidate answer passage (1).

Accordingly, candidate answer passage (3) is selected and provided as the answer passage of FIG. 2.

Boosting Scores Based on Passage Coverage Ratio

A query question processor may limit the candidate answers to a maximum length.

The context scoring processor determines a coverage ratio which is a measure indicative of the coverage of the candidate answer passage from the text from which it was selected.

The patent describes alternative question answers:

Alternatively, the text block may include text sections subordinate to respective headings that include a first heading for which the text section from which the candidate answer passage was selected is subordinate, and sibling headings that have an immediate parent heading in common with the first heading. For example, for the candidate answer passage, the text block may include all the text in the portion 380 of the hierarchy; or may include only the text of the sections, of some other portion of text within the portion of the hierarchy. A similar block may be used for the portion of the hierarchy for candidate answer passages selected from that portion.

A small coverage ratio may indicate a candidate answer passage is incomplete. A high coverage ratio may indicate the candidate answer passage captures more of the content of the text passage from which it was selected. A candidate answer passage may receive a context adjustment, depending on this coverage ratio.

A passage coverage ratio is a ratio of the total number of characters in the candidate answer passage to the ratio of the total number of characters in the passage from which the candidate answer passage was selected.

The passage cover ratio could also be a ratio of the total number of sentences (or words) in the candidate answer passage to the ratio of the total number of sentences (or words) in the passage from which the candidate answer passage was selected.

We are told that other ratios can also be used.

From the three example candidate answer passages about the distance to the moon above (1)-(3) above, passage (1) has the highest ratio, passage (2) has the second-highest, and passage (3) has the lowest.

This process determines whether the coverage ratio is less than a threshold value. That threshold value can be, for example, 0.3, 0.35 or 0.4, or some other fraction. In our “distance to the moon” example, each coverage passage ratio meets or exceeds the threshold value.

If the coverage ratio is less than a threshold value, then the process would select a first answer boost factor. The first answer boost factor might be proportional to the coverage ratio according to a first relation, or maybe a fixed value, or maybe a non-boosting value (e.g., 1.0.)

But if the coverage ratio is not less than the threshold value, the process may select a second answer boost factor. The second answer boost factor may be proportional to the coverage ratio according to a second relation, or maybe fixed value, or maybe a value greater than the non-boosting value (e.g., 1.1.)

Scoring Based on Other Features

The context scoring process can also check for the presence of features in addition to those described above.

Three example features for contextually scoring an answer passage can be based on the additional features of the distinctive text, a preceding question, and a list format.

Distinctive text

Distinctive text is the text that may stand out because it is formatted differently than other text, like using bolding.

A Preceeding Question

A preceding question is a question in the text that precedes the candidate answer question.

The search engine may process various amounts of text to detect for the question.

Only the passage from which the candidate answer passage is extracted is detected.

A text window that can include header text and other text from other sections may be checked.

A boost score that is inversely proportional to the text distance from a question to the candidate answer passage is calculated, and the check is terminated at the occurrence of a first question.

That text distance may be measured in characters, words, or sentences, or by some other metric.

If the question is anchor text for a section of text and there is intervening text, such as in the case of a navigation list, then the question is determined to only precede the text passage to which it links, not precede intervening text.

In the drawing above about the moon, there are two questions in the resource: “How long does it take for the Moon to orbit Earth?” and “Why is the distance changing?”

The first question–“How long does it take for the Moon to orbit Earth?”– precedes the first candidate answer passage by a text distance of zero sentences, and it precedes the second candidate answer passage by a text distance of five sentences.

And the second question–“Why is the distance changing?”– precedes the third candidate answer by zero sentences.

If a preceding question is detected, then the process selects a question boost factor.

This boost factor may be proportional to the text distance, whether the text is in a text passage subordinate to a header or whether the question is a header, and, if the question is in a header, whether the candidate answer passage is subordinate to the header.

Considering these factors, the third candidate answer passage receives the highest boost factor, the first candidate answer receives the second-highest boost factor, and the second candidate answer receives the smallest boost factor.

Conversely, if the preceding text is not detected, or after the question boost factor is detected, then the process detects for the presence of a list.

The Presence of a List

A list is an indication of several steps usually instructive or informative. The detection of a list may be subject to the query question being a step modal query.

A step modal query is a query where a list-based answer is likely to a good answer. Examples of step model queries are queries like:

  • [How to . . . ]
  • [How do I . . . ]
  • [How to install a door knob]
  • [How do I change a tire]

The context scoring process may detect lists formed with:

  • HTML tags
  • Micro formats
  • Semantic meaning
  • Consecutive headings at the same level with the same or similar phrases (e.g., Step 1, Step 2; or First; Second; Third; etc.)

The context scoring process may also score a list for quality.

It would look at things such as:

  • A list in the center of a page, which does not include multiple links to other pages (indicative of reference lists)
  • HREF link text that does not occupy a large portion of the text of the list will be of higher quality than a list at the side of a page, and which does include multiple links to other pages (which are indicative of reference lists), and/are has HREF link text that does occupy a large portion of the text of the list

If a list is detected, then the process selects a list boost factor.

That list boost factor may be fixed or may be proportional to the quality score of the list.

If a list is not detected, or after the list boost factor is selected, the process ends.

In some implementations, the list boost factor may also be dependent on other feature scores.

If other features, such as coverage ratio, distinctive text, etc., have relatively high scores, then the list boot factor may be increased.

The patent tells us that this is because “the combination of these scores in the presence of a list is a strong signal of a high-quality answer passage.”

Adjustment of Featured Snippet Answers Scores

Answer scores for candidate answer passages are adjusted by scoring components based on heading vectors, passage coverage ratio, and other features described above.

The scoring process can select the largest boost value from those determined above or can select a combination of the boost values.

Once the answer scores are adjusted, the candidate answer passage with the highest adjusted answer score is selected as the featured snippet answer and is displayed to a searcher.

More to Come

I will be reviewing the first patent in this series of patents about candidate answer scores because it does have some additional elements to it that haven’t been covered in this post, and the post about query dependent/independent ranking signals for answer scores. If you have been paying attention to how Google has been answering queries that appear to be seeking answers, you have likely seen those improving in many cases. Some answers have been really bad though. It will be nice to have as complete an idea as we can of how Google decides what might be a good answer to a query, based on information available to them on the Web.

Added October 14, 2020 – I have written about another Google patent on Answer Scores, and it’s worth reading about all of the patents on this topic. The new post is at Weighted Answer Terms for Scoring Answer Passages, and is about the patent Weighted answer terms for scoring answer passages.

It is about identifying questions in resources, and answers for those questions, and describes using term weights as a way to score answer passages (along with the scoring approaches identified in the other related patents, including this one.)

Added October 15, 2020 – I have written a few other posts about answer passages that are worth reading if you are interested in how Google finds questions on pages and answers to those, and scores answer passages to determine which ones to show as featured snippets. I’ve linked to some of those in the body of this post, but here is another one of those posts:

Added October 22, 2020, I have written up a description of details from about how structured and unstructured data has been selected for answer passages based on specific criteria in the patent on Scoring Answer passages in the post Selecting Candidate Answer Passages.


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Friday app, a remote work tool, raises $2.1 million led by Bessemer

November 29, 2020 No Comments

Friday, an app looking to make remote work more efficient, has announced the close of a $ 2.1 million seed round led by Bessemer Venture Partners. Active Capital, Underscore, El Cap Holdings, TLC Collective and New York Venture Partners also participated in the round, among others.

Founded by Luke Thomas, Friday sits on top of the tools that teams already use — GitHub, Trello, Asana, Slack, etc. — to surface information that workers need when they need it and keep them on top of what others in the organization are doing.

The platform offers a Daily Planner feature, so users can roadmap their day and share it with others, as well as a Work Routines feature, giving users the ability to customize and even automate routine updates. For example, weekly updates or daily standups done via Slack or Google Hangouts can be done via Friday app, eliminating the time spent by managers, or others, jotting down these updates or copying that info over from Slack.

Friday also lets users set goals across the organization or team so that users’ daily and weekly work aligns with the broader OKRs of the company.

Plus, Friday users can track their time spent in meetings, as well as team morale and productivity, using the Analytics dashboard of the platform.

Friday has a free-forever model, which allows individual users or even organizations to use the app for free for as long as they want. More advanced features like Goals, Analytics and the ability to see past three weeks of history within the app are paywalled for a price of $ 6/seat/month.

Thomas says that one of the biggest challenges for Friday is that people automatically assume it’s competing with an Asana or Trello, as opposed to being a layer on top of these products that brings all that information into one place.

“The number one problem is that we’re in a noisy space,” said Thomas. “There are a lot of tools that are saying they’re a remote work tool when they’re really just a layer on top of Zoom or a video conferencing tool. There is certainly increased amount of interest in the space in a good and positive way, but it also means that we have to work harder to cut through the noise.”

The Friday team is small for now — four full-time staff members — and Thomas says that he plans to double the size of the team following the seed round. Thomas declined to share any information around the diversity breakdown of the team.

Following a beta launch at the beginning of 2020, Friday says it is used by employees at organizations such as Twitter, LinkedIn, Quizlet, Red Hat and EA, among others.

This latest round brings the company’s total funding to $ 2.5 million.


Enterprise – TechCrunch


Wall Street needs to relax, as startups show remote work is here to stay

November 28, 2020 No Comments

We are hearing that a COVID-19 vaccine could be on the way sooner than later, and that means we could be returning to normal life some time in 2021. That’s the good news. The perplexing news, however, is that each time some positive news emerges about a vaccine — and believe me I’m not complaining — Wall Street punishes stocks it thinks benefits from us being stuck at home. That would be companies like Zoom and Peloton.

While I’m not here to give investment advice, I’m confident that these companies are going to be fine even after we return to the office. While we surely pine for human contact, office brainstorming, going out to lunch with colleagues and just meeting and collaborating in the same space, it doesn’t mean we will simply return to life as it was before the pandemic and spend five days a week in the office.

One thing is clear in my discussions with startups born or growing up during the pandemic: They have learned to operate, hire and sell remotely, and many say they will continue to be remote-first when the pandemic is over. Established larger public companies like Dropbox, Facebook, Twitter, Shopify and others have announced they will continue to offer a remote-work option going forward. There are many other such examples.

It’s fair to say that we learned many lessons about working from home over this year, and we will carry them with us whenever we return to school and the office — and some percentage of us will continue to work from home at least some of the time, while a fair number of businesses could become remote-first.

Wall Street reactions

On November 9, news that the Pfizer vaccine was at least 90% effective threw the markets for a loop. The summer trade, in which investors moved capital from traditional, non-tech industries and pushed it into software shares, flipped; suddenly the stocks that had been riding a pandemic wave were losing ground while old-fashioned, even stodgy, companies shot higher.


Enterprise – TechCrunch


Stay In and Get Cozy With These Black Friday Video Game Deals

November 28, 2020 No Comments

We need to stay indoors and not travel for the rest of the year. Why not stockpile on Switch, PlayStation, and Xbox titles?
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Adjusting Featured Snippet Answers by Context

November 28, 2020 No Comments

How Are Featured Snippet Answers Decided Upon?

I recently wrote about Featured Snippet Answer Scores Ranking Signals. In that post, I described how Google was likely using query dependent and query independent ranking signals to create answer scores for queries that were looking like they wanted answers.

One of the inventors of that patent from that post was Steven Baker. I looked at other patents that he had written, and noticed that one of those was about context as part of query independent ranking signals for answers.

Remembering that patent about question-answering and context, I felt it was worth reviewing that patent and writing about it.

This patent is about processing question queries that want textual answers and how those answers may be decided upon.

it is a complicated patent, and at one point the description behind it seems to get a bit murky, but I wrote about when that happened in the patent, and I think the other details provide a lot of insight into how Google is scoring featured snippet answers. There is an additional related patent that I will be following up with after this post, and I will link to it from here as well.

This patent starts by telling us that a search system can identify resources in response to queries submitted by users and provide information about the resources in a manner that is useful to the users.

How Context Scoring Adjustments for Featured Snippet Answers Works

Users of search systems are often searching for an answer to a specific question, rather than a listing of resources, like in this drawing from the patent, showing featured snippet answers:

featured snippet answers

For example, users may want to know what the weather is in a particular location, a current quote for a stock, the capital of a state, etc.

When queries that are in the form of a question are received, some search engines may perform specialized search operations in response to the question format of the query.

For example, some search engines may provide information responsive to such queries in the form of an “answer,” such as information provided in the form of a “one box” to a question, which is often a featured snippet answer.

Some question queries are better served by explanatory answers, which are also referred to as “long answers” or “answer passages.”

For example, for the question query [why is the sky blue], an answer explaining light as waves is helpful.

featured snippet answers - why is the sky blue

Such answer passages can be selected from resources that include text, such as paragraphs, that are relevant to the question and the answer.

Sections of the text are scored, and the section with the best score is selected as an answer.

In general, the patent tells us about one aspect of what it covers in the following process:

  • Receiving a query that is a question query seeking an answer response
  • Receiving candidate answer passages, each passage made of text selected from a text section subordinate to a heading on a resource, with a corresponding answer score
  • Determining a hierarchy of headings on a page, with two or more heading levels hierarchically arranged in parent-child relationships, where each heading level has one or more headings, a subheading of a respective heading is a child heading in a parent-child relationship and the respective heading is a parent heading in that relationship, and the heading hierarchy includes a root level corresponding to a root heading (for each candidate answer passage)
  • Determining a heading vector describing a path in the hierarchy of headings from the root heading to the respective heading to which the candidate answer passage is subordinate, determining a context score based, at least in part, on the heading vector, adjusting the answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score
  • Selecting an answer passage from the candidate answer passages based on the adjusted answer scores

Advantages of the process in the patent

  1. Long query answers can be selected, based partially on context signals indicating answers relevant to a question
  2. The context signals may be, in part, query-independent (i.e., scored independently of their relatedness to terms of the query
  3. This part of the scoring process considers the context of the document (“resource”) in which the answer text is located, accounting for relevancy signals that may not otherwise be accounted for during query-dependent scoring
  4. Following this approach, long answers that are more likely to satisfy a searcher’s informational need are more likely to appear as answers

This patent can be found at:

Context scoring adjustments for answer passages
Inventors: Nitin Gupta, Srinivasan Venkatachary , Lingkun Chu, and Steven D. Baker
US Patent: 9,959,315
Granted: May 1, 2018
Appl. No.: 14/169,960
Filed: January 31, 2014

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for context scoring adjustments for candidate answer passages.

In one aspect, a method includes scoring candidate answer passages. For each candidate answer passage, the system determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading to which the candidate answer passage is subordinate; determines a context score based, at least in part, on the heading vector; and adjusts answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score.

The system then selects an answer passage from the candidate answer passages based on the adjusted answer scores.

Using Context Scores to Adjust Answer Scores for Featured Snippets

A drawing from the patent shows different hierarchical headings that may be used to determine the context of answer passages that may be used to adjust answer scores for featured snippets:

Hierarchical headings for featured snippets

I discuss these headings and their hierarchy below. Note that the headings include the Page title as a heading (About the Moon), and the headings within heading elements on the page as well. And those headings give those answers context.

This context scoring process starts with receiving candidate answer passages and a score for each of the passages.

Those candidate answer passages and their respective scores are provided to a search engine that receives a query determined to be a question.

Each of those candidate answer passages is text selected from a text section under a particular heading from a specific resource (page) that has a certain answer score.

For each resource where a candidate answer passage has been selected, a context scoring process determines a heading hierarchy in the resource.

A heading is text or other data corresponding to a particular passage in the resource.

As an example, a heading can be text summarizing a section of text that immediately follows the heading (the heading describes what the text is about that follows it, or is contained within it.)

Headings may be indicated, for example, by specific formatting data, such as heading elements using HTML.

This next section from the patent reminded me of an observation that Cindy Krum of Mobile Moxie has about named anchors on a page, and how Google might index those to answer a question, to lead to an answer or a featured snippet. She wrote about those in What the Heck are Fraggles?

A heading could also be anchor text for an internal link (within the same page) that links to an anchor and corresponding text at some other position on the page.

A heading hierarchy could have two or more heading levels that are hierarchically arranged in parent-child relationships.

The first level, or the root heading, could be the title of the resource.

Each of the heading levels may have one or more headings, and a subheading of a respective heading is a child heading and the respective heading is a parent heading in the parent-child relationship.

For each candidate passage, a context scoring process may determine a context score based, at least in part, on the relationship between the root heading and the respective heading to which the candidate answer passage is subordinate.

The context scoring process could be used to determine the context score and determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading.

The context score could be based, at least in part, on the heading vector.

The context scoring process can then adjust the answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score.

The context scoring process can then select an answer passage from the candidate answer passages based on adjusted answer scores.

This flowchart from the patent shows the context scoring adjustment process:

context scoring adjustment flowchart

Identifying Question Queries And Answer Passages

I’ve written about understanding the context of answer passages. The patent tells us more about question queries and answer passages worth going over in more detail.

Some queries are in the form of a question or an implicit question.

For example, the query [distance of the earth from the moon] is in the form of an implicit question “What is the distance of the earth from the moon?”

An implicit question - the distance from the earth to the moon

Likewise, a question may be specific, as in the query [How far away is the moon].

The search engine includes a query question processor that uses processes that determine if a query is a query question (implicit or specific) and if it is, whether there are answers that are responsive to the question.

The query question processor can use several different algorithms to determine whether a query is a question and whether there are particular answers responsive to the question.

For example, it may use to determine question queries and answers:

  • Language models
  • Machine learned processes
  • Knowledge graphs
  • Grammars
  • Combinations of those

The query question processor may choose candidate answer passages in addition to or instead of answer facts. For example, for the query [how far away is the moon], an answer fact is 238,900 miles. And the search engine may just show that factual information since that is the average distance of the Earth from the moon.

But, the query question processor may choose to identify passages that are to be very relevant to the question query.

These passages are called candidate answer passages.

The answer passages are scored, and one passage is selected based on these scores and provided in response to the query.

An answer passage may be scored, and that score may be adjusted based on a context, which is the point behind this patent.

Often Google will identify several candidate answer passages that could be used as featured snippet answers.

Google may look at the information on the pages where those answers come from to better understand the context of the answers such as the title of the page, and the headings about the content that the answer was found within.

Contextual Scoring Adjustments for Featured Snippet Answers

The query question processor sends to a context scoring processor some candidate answer passages, information about the resources from which each answer passages was from, and a score for each of the featured snippet answers.

The scores of the candidate answer passages could be based on the following considerations:

  • Matching a query term to the text of the candidate answer passage
  • Matching answer terms to the text of the candidate answer passages
  • The quality of the underlying resource from which the candidate answer passage was selected

I recently wrote about featured snippet answer scores, and how a combination of query dependent and query independent scoring signals might be used to generate answer scores for answer passages.

The patent tells us that the query question processor may also take into account other factors when scoring candidate answer passages.

Candidate answer passages can be selected from the text of a particular section of the resource. And the query question processor could choose more than one candidate answer passage from a text section.

We are given the following examples of different answer passages from the same page

(These example answer passages are referred to in a few places in the remainder of the post.)

  • (1) It takes about 27 days (27 days, 7 hours, 43 minutes, and 11.6 seconds) for the Moon to orbit the Earth at its orbital distance
  • (2) Why is the distance changing? The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
  • (3) The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles

Each of those answers could be good ones for Google to use. We are told that:

More than three candidate answers can be selected from the resource, and more than one resource can be processed for candidate answers.

How would Google choose between those three possible answers?

Google might decide based on the number of sentences and a selection of up to a maximum number of characters.

The patent tells us this about choosing between those answers:

Each candidate answer has a corresponding score. For this example, assume that candidate answer passage (2) has the highest score, followed by candidate answer passage (3), and then by candidate answer passage (1). Thus, without the context scoring processor, candidate answer passage (2) would have been provided in the answer box of FIG. 2. However, the context scoring processor takes into account the context of the answer passages and adjusts the scores provided by the query question processor.

So, we see that what might be chosen based on featured snippet answer scores could be adjusted based on the context of that answer from the page that it appears on.

Contextually Scoring Featured Snippet Answers

This process starts which begins with a query determined to be a question query seeking an answer response.

This process next receives candidate answer passages, each candidate answer passage chosen from the text of a resource.

Each of the candidate answer passages are text chosen from a text section that is subordinate to a respective heading (under a heading) in the resource and has a corresponding answer score.

For example, the query question processor provides the candidate answer passages, and their corresponding scores, to the context scoring processor.

A Heading Hierarchy to Determine Context

This process then determines a heading hierarchy from the resource.

The heading hierarchy would have two or more heading levels hierarchically arranged in parent-child relationships (Such as a page title, and an HTML heading element.)

Each heading level has one or more headings.

A subheading of a respective heading is a child heading (an (h2) heading might be a subheading of a (title)) in the parent-child relationship and the respective heading is a parent heading in the relationship.

The heading hierarchy includes a root level corresponding to a root heading.

The context scoring processor can process heading tags in a DOM tree to determine a heading hierarchy.

hierarchical headings for featured snippets

For example, concerning the drawing about the distance to the moon just above, the heading hierarchy for the resource may be:

The ROOT Heading (title) is: About The Moon (310)

The main heading (H1) on the page

H1: The Moon’s Orbit (330)

A secondary heading (h2) on the page:

H2: How long does it take for the Moon to orbit Earth? (334)

Another secondary heading (h2) on the page is:

H2: The distance from the Earth to the Moon (338)

Another Main heading (h1) on the page

H1: The Moon (360)

Another secondary Heading (h2) on the page:

H2: Age of the Moon (364)

Another secondary heading (h2) on the page:

H2: Life on the Moon (368)

Here is how the patent describes this heading hierarchy:

In this heading hierarchy, The title is the root heading at the root level; headings 330 and 360 are child headings of the heading, and are at a first level below the root level; headings 334 and 338 are child headings of the heading 330, and are at a second level that is one level below the first level, and two levels below the root level; and headings 364 and 368 are child headings of the heading 360 and are at a second level that is one level below the first level, and two levels below the root level.

The process from the patent determines a context score based, at least in part, on the relationship between the root heading and the respective heading to which the candidate answer passage is subordinate.

This score may be is based on a heading vector.

The patent says that the process, for each of the candidate answer passages, determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading.

The heading vector would include the text of the headings for the candidate answer passage.

For the example candidate answer passages (1)-(3) above about how long it takes the moon to orbit the earch, the respectively corresponding heading vectors V1, V2 and V3 are:

  • V1=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: How long does it take for the Moon to orbit the Earth?]>
  • V2=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: The distance from the Earth to the Moon]>
  • V3=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: The distance from the Earth to the Moon]>

We are also told that because candidate answer passages (2) and (3) are selected from the same text section 340, their respective heading vectors V2 and V3 are the same (they are both in the content under the same (H2) heading.)

The process of adjusting a score, for each answer passage, uses a context score based, at least in part, on the heading vector (410).

That context score can be a single score used to scale the candidate answer passage score or can be a series of discrete scores/boosts that can be used to adjust the score of the candidate answer passage.

Where things Get Murky in This Patent

There do seem to be several related patents involving featured snippet answers, and this one which targets learning more about answers from their context based on where they fit in a heading hierarchy makes sense.

But, I’m confused by how the patent tells us that one answer based on the context would be adjusted over another one.

The first issue I have is that the answers they are comparing in the same contextual area have some overlap. Here those two are:

  • (2) Why is the distance changing? The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
  • (3) The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles

Note that the second answer and the third answer both include the same line: “Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles.” I find myself a little surprised that the second answer includes a couple of sentences that aren’t in the third answer, and skips a couple of lines from the third answer, and then includes the last sentence, which answers the question.

Since they both appear in the same heading and subheading section of the page they are from, it is difficult to imagine that there is a different adjustment based on context. But, the patent tells us differently:

The candidate answer score with the highest adjusted answer score (based on context from the headings) is selected, and the answer passage.

Recall that in the example above, the candidate answer passage (2) had the highest score, followed by candidate answer passage (3), and then by candidate answer passage (1).

However, after adjustments, candidate answer passage (3) has the highest score, followed by candidate answer passage (2), and then-candidate answer passage (1).

Accordingly, candidate answer passage (3) is selected and provided as the answer passage of FIG. 2.

Boosting Scores Based on Passage Coverage Ratio

A query question processor may limit the candidate answers to a maximum length.

The context scoring processor determines a coverage ratio which is a measure indicative of the coverage of the candidate answer passage from the text from which it was selected.

The patent describes alternative question answers:

Alternatively, the text block may include text sections subordinate to respective headings that include a first heading for which the text section from which the candidate answer passage was selected is subordinate, and sibling headings that have an immediate parent heading in common with the first heading. For example, for the candidate answer passage, the text block may include all the text in the portion 380 of the hierarchy; or may include only the text of the sections, of some other portion of text within the portion of the hierarchy. A similar block may be used for the portion of the hierarchy for candidate answer passages selected from that portion.

A small coverage ratio may indicate a candidate answer passage is incomplete. A high coverage ratio may indicate the candidate answer passage captures more of the content of the text passage from which it was selected. A candidate answer passage may receive a context adjustment, depending on this coverage ratio.

A passage coverage ratio is a ratio of the total number of characters in the candidate answer passage to the ratio of the total number of characters in the passage from which the candidate answer passage was selected.

The passage cover ratio could also be a ratio of the total number of sentences (or words) in the candidate answer passage to the ratio of the total number of sentences (or words) in the passage from which the candidate answer passage was selected.

We are told that other ratios can also be used.

From the three example candidate answer passages about the distance to the moon above (1)-(3) above, passage (1) has the highest ratio, passage (2) has the second-highest, and passage (3) has the lowest.

This process determines whether the coverage ratio is less than a threshold value. That threshold value can be, for example, 0.3, 0.35 or 0.4, or some other fraction. In our “distance to the moon” example, each coverage passage ratio meets or exceeds the threshold value.

If the coverage ratio is less than a threshold value, then the process would select a first answer boost factor. The first answer boost factor might be proportional to the coverage ratio according to a first relation, or maybe a fixed value, or maybe a non-boosting value (e.g., 1.0.)

But if the coverage ratio is not less than the threshold value, the process may select a second answer boost factor. The second answer boost factor may be proportional to the coverage ratio according to a second relation, or maybe fixed value, or maybe a value greater than the non-boosting value (e.g., 1.1.)

Scoring Based on Other Features

The context scoring process can also check for the presence of features in addition to those described above.

Three example features for contextually scoring an answer passage can be based on the additional features of the distinctive text, a preceding question, and a list format.

Distinctive text

Distinctive text is the text that may stand out because it is formatted differently than other text, like using bolding.

A Preceeding Question

A preceding question is a question in the text that precedes the candidate answer question.

The search engine may process various amounts of text to detect for the question.

Only the passage from which the candidate answer passage is extracted is detected.

A text window that can include header text and other text from other sections may be checked.

A boost score that is inversely proportional to the text distance from a question to the candidate answer passage is calculated, and the check is terminated at the occurrence of a first question.

That text distance may be measured in characters, words, or sentences, or by some other metric.

If the question is anchor text for a section of text and there is intervening text, such as in the case of a navigation list, then the question is determined to only precede the text passage to which it links, not precede intervening text.

In the drawing above about the moon, there are two questions in the resource: “How long does it take for the Moon to orbit Earth?” and “Why is the distance changing?”

The first question–“How long does it take for the Moon to orbit Earth?”– precedes the first candidate answer passage by a text distance of zero sentences, and it precedes the second candidate answer passage by a text distance of five sentences.

And the second question–“Why is the distance changing?”– precedes the third candidate answer by zero sentences.

If a preceding question is detected, then the process selects a question boost factor.

This boost factor may be proportional to the text distance, whether the text is in a text passage subordinate to a header or whether the question is a header, and, if the question is in a header, whether the candidate answer passage is subordinate to the header.

Considering these factors, the third candidate answer passage receives the highest boost factor, the first candidate answer receives the second-highest boost factor, and the second candidate answer receives the smallest boost factor.

Conversely, if the preceding text is not detected, or after the question boost factor is detected, then the process detects for the presence of a list.

The Presence of a List

A list is an indication of several steps usually instructive or informative. The detection of a list may be subject to the query question being a step modal query.

A step modal query is a query where a list-based answer is likely to a good answer. Examples of step model queries are queries like:

  • [How to . . . ]
  • [How do I . . . ]
  • [How to install a door knob]
  • [How do I change a tire]

The context scoring process may detect lists formed with:

  • HTML tags
  • Micro formats
  • Semantic meaning
  • Consecutive headings at the same level with the same or similar phrases (e.g., Step 1, Step 2; or First; Second; Third; etc.)

The context scoring process may also score a list for quality.

It would look at things such as:

  • A list in the center of a page, which does not include multiple links to other pages (indicative of reference lists)
  • HREF link text that does not occupy a large portion of the text of the list will be of higher quality than a list at the side of a page, and which does include multiple links to other pages (which are indicative of reference lists), and/are has HREF link text that does occupy a large portion of the text of the list

If a list is detected, then the process selects a list boost factor.

That list boost factor may be fixed or may be proportional to the quality score of the list.

If a list is not detected, or after the list boost factor is selected, the process ends.

In some implementations, the list boost factor may also be dependent on other feature scores.

If other features, such as coverage ratio, distinctive text, etc., have relatively high scores, then the list boot factor may be increased.

The patent tells us that this is because “the combination of these scores in the presence of a list is a strong signal of a high-quality answer passage.”

Adjustment of Featured Snippet Answers Scores

Answer scores for candidate answer passages are adjusted by scoring components based on heading vectors, passage coverage ratio, and other features described above.

The scoring process can select the largest boost value from those determined above or can select a combination of the boost values.

Once the answer scores are adjusted, the candidate answer passage with the highest adjusted answer score is selected as the featured snippet answer and is displayed to a searcher.

More to Come

I will be reviewing the first patent in this series of patents about candidate answer scores because it does have some additional elements to it that haven’t been covered in this post, and the post about query dependent/independent ranking signals for answer scores. If you have been paying attention to how Google has been answering queries that appear to be seeking answers, you have likely seen those improving in many cases. Some answers have been really bad though. It will be nice to have as complete an idea as we can of how Google decides what might be a good answer to a query, based on information available to them on the Web.

Added October 14, 2020 – I have written about another Google patent on Answer Scores, and it’s worth reading about all of the patents on this topic. The new post is at Weighted Answer Terms for Scoring Answer Passages, and is about the patent Weighted answer terms for scoring answer passages.

It is about identifying questions in resources, and answers for those questions, and describes using term weights as a way to score answer passages (along with the scoring approaches identified in the other related patents, including this one.)

Added October 15, 2020 – I have written a few other posts about answer passages that are worth reading if you are interested in how Google finds questions on pages and answers to those, and scores answer passages to determine which ones to show as featured snippets. I’ve linked to some of those in the body of this post, but here is another one of those posts:

Added October 22, 2020, I have written up a description of details from about how structured and unstructured data has been selected for answer passages based on specific criteria in the patent on Scoring Answer passages in the post Selecting Candidate Answer Passages.


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Adobe expands customer data platform to include B2B sales

November 27, 2020 No Comments

The concept of the customer data platform (CDP) is a relatively new one. Up until now, it has focused primarily on pulling data about an individual consumer from a variety of channels into a super record, where in theory you can serve more meaningful content and deliver more customized experiences based on all this detailed knowledge. Adobe announced its intention today to create such a product for business to business (B2B) customers, a key market where this kind of data consolidation had been missing.

Indeed, Brian Glover, Adobe’s director of product marketing for Marketo Engage, who has been put in charge of this product, says that these kinds of sales are much more complex and B2B sales and marketing teams are clamoring for a CDP.

“We have spent the last couple of years integrating Marketo Engage across Adobe Experience Cloud, and now what we’re doing is building out the next generation of new and complementary B2B offerings on the Experience platform, the first of which is the B2B CDP offering,” Glover told me.

He says that they face unique challenges adapting CDP for B2B sales because they typically involve buying groups, meaning you need to customize your messages for different people depending on their role in the process.

An individual consumer usually knows what they want and you can prod them to make a decision and complete the purchase, but a B2B sale is usually longer and more complex, involving different levels of procurement. For example, in a technology sale, it may involve the CIO, a group, division or department who will be using the tech, the finance department, legal and others. There may be an RFP and the sales cycle may span months or even years.

Adobe believes this kind of sale should still be able to use the same customized messaging approach you use in an individual sale, perhaps even more so because of the inherent complexity in the process. Yet B2B marketers face the same issues as their B2C counterparts when it comes to having data spread across an organization.

“In B2B that complexity of buying groups and accounts just adds another level to the data management problem because ultimately you need to be able to connect to your customer people data, but you also need to be able to connect the account data too and be able to [bring] the two together,” Glover explained.

By building a more complete picture of each individual in the buying cycle, you can, as Glover puts it, begin to put the bread crumbs together for the entire account. He believes that a CRM isn’t built for this kind of complexity and it requires a specialty tool like a CDP built to support B2B sales and marketing.

Adobe is working with early customers on the product and expects to go into beta before the end of next month with GA some time in the first half of next year.


Enterprise – TechCrunch


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