Author: Michael Andrews

When authors tell stories, verbs provide the action. Verbs move audiences. We want to know “what happened next?” But verbs are hard to categorize in ways computers understand and can act on. Despite that challenge, verbs are important enough that we must work harder to capture their intent, so we can align content with the needs of audiences. I will propose two approaches to overcome these challenges: task-focused and situational taxonomies. These approaches involve identifying the “key verbs” in our content.

Nouns and Verbs in Writing

I recently re-read a classic book on writing by Sir Ernest Gowers entitled The Complete Plain Words. Published immediately after the Second World War, the book was one of the first to advocate the use of plain language.

Gowers attacks obtuse, abstract writing. He quotes with approval a now forgotten essayist G.M. Young:

“Excessive reliance on the noun at the expense of the verb will in the end detach the mind of the writer from the realities of here and now, from when and how, and in what mood this thing was done and insensibly induce a habit of abstraction, generalization and vagueness.”

If we look past the delicious irony — a critique of abstraction that is abstract — we learn that writing that emphasizes verbs is vivid.

Gowers refers to this snippet as an example of abstract writing:

• “Communities where anonymity in personal relationships prevails.”

Instead, he says the wording should be:

• “Communities where people do not know one another.”

Without doubt the second example is easier to read, and feels more relevant to us as individuals. But the meaning of the two sentences, while broadly similar, is subtly different. We can see this by diagramming the key components. The strengthening of the verb in the second example has the effect of making the subject and object more vague.

It is easy to see the themes in the first example, which are explicitly called out. The first diagram highlights themes of anonymity and personal relationships in a way the second diagram does not. The different levels of detail in the wording will draw attention to different dimensions.

With a more familiar style of writing, the subject is often personalized or implied. The subject is about you, or people like you. This may be one reason why lawyers and government officials like to use abstract words. They aren’t telling a specific story; they are trying to make a point about a more general concept.

Abstract vs Familiar Styles

I will make a simple argument. Abstract writing focuses on nouns, which are easier for computers to understand. Conversely, content written in a familiar style is more difficult for computers to understand and act on. Obviously, people — and not computers — are the audience for our content. I am not advocating an abstract style of writing. But we should understand and manage the challenges that familiar styles of writing pose for computers. Computers do matter. Until natural language processing by computers truly matches human abilities, humans are going to need to help computers understand what we mean. Because it’s hard for computers to understand discussions about actions, it is even more important that we have metadata that describes those actions.

The table below summarizes the orientations of each style. These sweeping characterizations won’t be true in all cases. Nonetheless, these tendencies are prevalent, and longstanding.

	Abstract Style	Familiar Style
Emphasis	Nouns General concepts Reader is outside of the article context	Verbs Specific advice Reader is within the article context
Major uses	Represents a class of concepts or events Good for navigation	Shows instance of a concept or event Good for reading
Benefits	Promotes analytic tracking Promotes automated content recommendations	Promotes content engagement Promotes social referrals
Limitations	Can trigger weak writing	Can trigger weak metadata

These tendencies are not destiny. Steven Pinker, the MIT cognitive scientist turned prose guru, can write about abstract topics in an accessible manner — he makes an effort to do so. Likewise, it is possible to develop good metadata for narrative content. It requires the ability to sense what is missing and implied.

Challenges of a Taxonomy of Verbs

Why is metadata difficult for narrative content? Why is so much metadata tilted toward abstract content? There are three main issues:

• Indexing relies on basic vocabulary matching
• Taxonomies are noun-centric
• Verbs are difficult to specify

Indexing and Vocabulary Matching

Computers rely on indexes to identify content. Metadata is a type of index that identifies and describes the content. Metadata indexes may be based the manual tagging of content (application of metadata) with descriptive terms, or be based on auto-indexing and auto-categorization.

Computers can easily identify and index nouns, often referred to as entities. Named entity recognition can identify proper nouns such as personal names. It is also comparatively easy to identify common nouns in a text when a list of nouns of interest has been identified ahead of time. This is done either through string indexing (matching the character string to the index term) or assigned indexing (matching a character string to a concept term that has been identified as equivalent.)

The manual tagging of entities is also straightforward. A person will identify the major nouns used in a text, and select the appropriate index term that corresponds to the noun. When they decide what things are most important in the article (often the things mentioned most frequently), they find tags that describe those things.

When the text has entities that are proper or common nouns, it isn’t too hard to identify which ones are important and should be indexed. Abstract content is loaded with such nouns, and computers (and people) have an easy time identifying key words that describe the content. But as we will see, when the meaning of a text is based on the heavy use of pronouns and descriptive verbs, the task of matching terms to an index vocabulary becomes more difficult. Narrative content, where verbs are especially important to the meaning, is challenging to index. Nouns are easier to decipher than verbs.

Taxonomies are Noun-centric

When we offer a one-word description, we tend to label stuff using nouns. The headings in an encyclopedia are nouns. Taxonomies similarly rely on nouns to identify what an article is about. It’s our default way of thinking about descriptions.

Because we focus on the nouns, we can easily overlook the meaning carried by the verbs when tagging our content. But verbs can carry great meaning. Consider an article entitled “How to feel more energetic.” There are no nouns in the title to match up with taxonomy terms. Depending on the actual content of the article, it might relate to exercise, or diet, or mental attitude, but those topics are secondary in purpose to the theme of the article, which is about feeling better. A taxonomy may have granular detail, and include a thesaurus of equivalent and related terms, but the most critical issue is that the explicit wording of the article can be translated into the vocabulary used in the taxonomy.

Verbs are Difficult to Specify

Verbs also can be included in descriptive vocabularies for content, but they are more challenging to use. Verbs are sometimes looser in meaning than nouns. Sometimes they are figurative.

A verb may have many meanings. These meanings are sometimes fuzzy. Actions and sentiments can be described by multiple verbs and verbal phrases. Consider the most overworked and meaningless verb used on the web today: to like. If Ralph “likes” this, what does that really mean? Compared to what else? The English language has a range of nuanced verbs (love, being fond of, being interested in, being obsessed with, etc.) to express positive sentiment, though it is hard to demarcate their exact equivalences and differences.

Many common verbs (such as work, make or do) have a multitude of meanings. When the meaning of a verbs is nebulous, it is takes more work to identify the preferred synonym used in a taxonomy. Consider this example from a text-tagging tool. The person reading the text needs to make the mental leap that the verb “moving” refers to “money transfer.” The task is not simply to match a word, but to represent a concept for an activity. We often use imprecise verb like move instead of more precise verb like transfer money. Such verbal informality makes tagging more difficult.

With the semantic web, predicates play the role of verbs defining the relationship between subjects and objects. The predicates can have many variants to express related concepts. If we say, “Jane Blogs was formerly married to Joe Blogs,” we don’t know what other verbal phrase would be equivalent. Did Jane Blogs divorce Joe Blogs? Did Joe Blogs die? Another piece of information may be needed to infer the full meaning. Verbal phrases can carry a degree of ambiguity, and this makes using a standard vocabulary for verbs harder to do.

Samuel Goto, a software engineer at Google, has said: “Verbs … they are kind of weird.”

Computers can’t understand verbs easily. Verb concepts are challenging for humans to describe with standardized vocabulary. Tagging verbs requires thought.

Why Verb Metadata Matters

If verbs are a pain to tag, why bother? So we can satisfy both the needs of our audiences and the needs of the computers that must be able to offer our audiences precisely what they want. As an organization, we need to make sure all this is happening effectively. We need to harmonize three buckets of needs: audience, IT, and brand.

Audience needs: Most audiences expect content written in familiar style, and want content with strong, active verbs. Those verbs often carry a big share of the meaning of what you are communicating. Audiences also want precise content, rather than hoping to stumble on something they like by accident. This requires good metadata.

IT needs: Computers have trouble understanding the meaning of verbs. Computers need a good taxonomy to support navigation through the content, and deliver good recommendations.

Brand needs: Brands need to be able to manage and analyze content according to the activities discussed in the content, not just the static nouns mentioned in it. If they don’t have a plan in place to identify key verbs in their content, and tag their meaning, they run the risk of having a hollow taxonomy that doesn’t deliver the results needed.

A solution to these competing needs is to have our metadata represent the actions mentioned in the content. I’m calling this approach finding your key verbs.[1]

Approaches to a Metadata of Actions

Two approaches are available to represent verb concepts. The first is to make verbs part of your taxonomy. The second is to translate verbs in your content into nouns in your taxonomy.

Task-focused Taxonomies

The first approach is to develop a list of verbs that express the actions discussed in your content. Starting with the general topics about which you produce content, you can do an analysis and see what specific activities the content discusses. We’ll call these activities “tasks.”

Think about the main tasks for the people we want to reach. How do they talk about these tasks? People don’t label themselves as a new-home buyer: they are looking for a new home. They may never actually buy, but they are looking. Verbs help us focus on what the story is. There may be sub tasks that our reader would do, and would want to read about. Not only are they looking for a new home, they are evaluating kitchens and getting recommendations on renovations. This task focus is important to help us manage content components, and track their value to audience segments. We can do this using a task-focused taxonomy.

I am aware of two general-purpose taxonomies that incorporate verbs. The tasks these taxonomies address may differ from your needs, but they may provide a starting point for building your own.

The new “actions” vocabulary available in schema.org is the better known of the two. Schema.org has identified around 100 actions “to describe what can be done with resources.” The purpose is to be able not only to find content items according to the action discussed, but to enable actions to be taken with the content. As a simple example, you might find an event, and click a button to confirm your attendance. Behind the scenes, that action will be managed by the vocabulary.

The schema actions are diverse. Some describe high-level activities such as to travel, while others refer to very granular activities, such as to follow somebody on a social network. Some task are real world tasks, and others strictly digital ones. I presume real-world actions are included to support activity-reporting from the Internet of Things (IoT) devices that monitor real-world phenomena such as exercise.

Framenet, a semantic tagging vocabulary used by linguists, is a another general vocabulary that provides coverage of verbs. If a sentence uses the verb “freeze” (in the sense of “to stop”), it is tagged with the concept of “activity_pause.” It is easiest to see how Framenet verb vocabulary works using an example from David Caswell’s project, Structured Stories. Verbs that encapsulate events form the core of each story element. [2]

Applications of Task Taxonomies

While both these vocabularies describe actions at the sentence or statement level, they can be applied to an entire article or section of content as well.

A task focus offers several benefits. Brands can track and manage content about activities independently of who specifically is featured doing the activity, or where/what the object or outcome of the activity is. So if brands produce content discussing options to travel, they might want to examine the performance of travel as a theme, rather than the variants of who travels or where they travel.

Task taxonomies also enable task-focused navigation, which lets people to start with an activity, then narrow down aspects of it. A sequence might start: What do you want to do? Then ask: Where would you like to do that? The sequence can work in reverse as well: people can discover something of interest (a destination) and then want to explore what to do there (a task).

Situational taxonomies

A second option uses nouns to indicate the notable events or situations discussed. Using nouns as proxies for actions unfortunately doesn’t capture a sense of dynamic movement. But if you can’t support a faceted taxonomy that can mix nouns and verbs, it may be the most practical option. When you have a list of descriptors that express actions discussed in your content, you are more likely to tag these qualities than if your taxonomy is entirely thing-centric. I’ll call a taxonomy that represents occasions using noun phrases a situational taxonomy. The terms in a situational taxonomy describe situations and events that may involve one or more activities.

If you have ever done business process modeling, you are familiar with the idea of describing things as passing through a routine lifecycle. We reify activities by giving them statuses: a project activity is under development, in review, launched, and so on. Many dimensions of our work and life involve routines with stages or statuses. When we produce content about these dimensions, we should tag the situation discussed.

One way to develop a situational taxonomy is by creating a blueprint of a detailed user journey that includes an end-to-end analysis of the various stages that real-world users go through, including the “unhappy path” where they encounter a situation they don’t want. Andrew Hinton has made a compelling case in his book Understanding Context that the situations that people find themselves in drive the needs they have. Many user journey maps don’t name the circumstances, they jump immediately into the actions people might do. Try to avoid doing that. Name each distinct situation: both the ones actively chosen by them as well as those foisted on them. Then map these terms to your content.

Situational taxonomies are suited to content about third parties (news for example) or when emphasizing the outcomes of a process rather than the factors that shape it. Processes that are complex or involve chance (financial gyrations or a health misfortune, for example) are suited to situational taxonomies. A situational taxonomy term describes “what happened?” at a high level. Thinking about events as a process or sequence can help to identify terms to describe the action discussed in the content.

The technical word for making nouns out of verbs is “nominalization.” For example, the verb “decide” becomes the noun “decision.” Not all nominalizations are equal: some are very clunky or empty of meaning. Decision is a better word than determination, for example. Try to keep situational terms from becoming too abstract.

Situational taxonomies are less granular than task-based ones. They provide an umbrella term that can represent several related actions. They can enhance tracking, navigation and recommendations, but not as precisely as task-based terms. Task taxonomies express more, suggesting not only what happens, but also how it happens.

Key Verbs Mark the Purpose of the Content

Identifying key verbs can be challenging work. Not all headlines will contain verbs. But ideally the opening paragraph should reveal verbs that frame the purpose of the article. Content strategists know that too much content is created without a well-defined purpose. Taxonomy terms focused on actions indicate what happens in the content, and suggest why that matters. Headlines, and taxonomy terms that rely entirely on nouns, don’t offer that.

We will look at some text from an animal shelter. I have intentionally removed the headline so we can focus on the content, to find the core concepts discussed. A simple part-of-speech app will allow us to isolate different kinds of words. First we will focus on the verbs in the text, which includes the terms “match”, “spot”, “suit”, “ask”, and “arrange”. The verb focus seems to be “matching.” Matching could be a good candidate term in a task taxonomy.

Now we’ll look at nouns. In additional to common nouns such as dogs and families, we see some nouns that suggest a process. Specifically, several nouns include the word “adoption.” Adoption would be a candidate term in a situational taxonomy. Note the shift in focus: adoption suggests a broader discussion about the process, whereas matching suggests a more specific goal.

When you look at content through the lens of verbs, questions arise. What verbs capture what the content is describing? Why is the content here? What is the reader or viewer supposed to do with this information? Could they tell someone else what is said here?

If you are having trouble finding key verbs, that could indicate problems with the content. Your content may not describe an activity. There is plenty of content that is “background content,” where readers are not expected to take any action after reading the content. If your goal for producing the content is simply to offer information for reference purposes, then it is unlikely you will find key verbs, because the content will probably be very noun-centric. The other possibility is that the writing is not organized clearly, and so key actions discussed are not readily seen. Both possibilities suggest a strategy check-up might be useful.

Avoid a Hollow Taxonomy

Even when tagging well-written content, capturing what activity is represented will require some effort. This can’t be automated, and the people doing the tagging need to pay close attention to what is implied in the content. They are identifying concepts, not simply matching words.

Tagging is easier to do when one already has vocabulary to describe the activities mentioned in your content. That requires auditing, discovery and planning. If your taxonomy only addresses things and not actions, it may be hollow. It can have gaps.

Most content is created to deliver an outcome. Metadata shouldn’t only describe the things that are mentioned. It should describe the actions that the content discusses, which will be explicitly or implicitly related to the actions you would like your customers to take. You want to articulate within metadata the intent of the content, and thus be in a position to use the content more effectively as a result. Key verbs let you capture the essence of your content.

By identifying key verbs, brands can use active terminology in their metadata to deliver content that is aligned with the intent of audiences.

The Future Web of Verbs

Web search is moving “from the noun to the verb,” according to Prabhakar Raghavan, Google’s Vice President of Engineering.

We are at the start of a movement toward a web of verbs, the fusing of content and actions. Taxonomy is moving away from its bookish origins as the practice of describing documents. Its future will increasingly be focused on supporting user actions, not just finding content. But before we can reach that stage, we need to understand the relationship between the content and actions of interest to the user.

Taxonomies need to reflect the intent of the user. We can understand that intent better when we can track content according to the actions it discusses. We can serve that intent better when we can offer options (recommendations or choices) centered on the actions of greatest interest to the user.

The first area that verb taxonomies will be implemented will likely be transactional ones, such as making reservations using Schema actions. But the applications are much broader than these “bottom of the funnel” interventions. Brands should start to think about using action-oriented taxonomy terms through their content offerings. This is an uncharted area, linking our metadata to our desired content outcomes.

— Michael Andrews