How to use AI for custom content tagging
Most of us perhaps have a general understanding of AI, short for Artificial Intelligence.
Though we have been witness to tremendous changes in our lifestyles thanks to the rapid advances digital technology has made in just one generation, the computing ability of that wonderful organ, the human brain, remains unsurpassed. Not that digital technology does not have advantages over the human brain. It can process much larger volumes than one single brain. It can apply defined rules, without fail, transaction after transaction. It does not tire or bore, of doing the same task over and over again.
And this is mostly with reference to what we call structured data. Software tools are able to understand characters when they are input in a structured format, what we often refer to as software code or software programs. Software can identify these characters and, based on their arrangement, interpret their meaning and take action based on those meanings.
However, when we come to unstructured information, software is not able to match the human brain. The effort has been ongoing now for many years for enabling machines to understand, interpret and act upon unstructured information, and to update themselves based on the additional inputs they keep receiving. This is usually referred to as AI, the ability of a machine to think like the human brain. We are already seeing AI being used in various types of software solutions. If we search for an airline ticket from New York to Chicago, you will perhaps start seeing advertisements for hotels and cab companies in Chicago. Stepping aside the issue of invasiveness of technology for a moment, this is an example of AI, where the software is able to understand that if you are traveling to Chicago, you will perhaps need a hotel to stay and a car to move around in.
There are many applications for AI, many under advanced stages of exploration. AI for custom content tagging is one of them.
Operating from geographies long considered the most suitable for data based BPO, oWorkers offers a host of services to its clients, in over 22 languages, among them content tagging.
What is content tagging?
A content management system could be described as a software that is used to produce and read, as well as do a number of other actions on, content on the web. A content tag is a term or identifier or locator added to it so that it becomes grouped with other pieces of content that might have been similarly tagged. It could be seen as a tool for content classification.
For example, a blogger may be publishing a blog based on sport, in which she writes about various sports and different aspects of each. Let us say she writes mostly on tennis, football and baseball. Some of her posts could be on games that have been recently played. Some could be on the careers of major players in the sport. Some might focus on team composition, some on new signings for the season, and so on and so forth.
She can tag her posts in many different ways, based on its contents and based on the profile of her readers. The most obvious one would be to tag it based on the sport it is about; tennis, football or baseball. She could also tag it with the names of the major players that feature in each post, say Roger Federer or Rafael Nadal or Novak Djokovic. With this tagging, if a reader is interested in posts about Djokovic, he can simply click on that tag and directly access all posts that have been similarly tagged.
A hierarchy of tags can also be used. For instance, the player’s name as tag may be a second level tag, after the main tag of the sport has been chosen.
A content tag, then, becomes an item that is visible to the audience or readership, and often appears in the form of a hyperlink that can be clicked to access its underlying contents. The primary objective of tags is not good SEO karma. They are used more from the perspective of end users and make filtering and accessing content easier for them.
While much of content tagging has been done manually, AI for custom content tagging is now increasingly being used to make content structured and useful.
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Benefits of tagging content
Here are some benefits content owning organizations might hope to realize from their efforts, regardless of whether AI for custom content tagging has been used in the exercise or not:
- It keeps content relevant for a long time to come. While content may have been created based on circumstances and events at a point in time, which will gradually fade, the tags this content gets associated with can be expected to have a much longer life.
- It creates a sort of common platform for content to be accessible through, regardless of the technology platform it resides on or the authoring tools used.
- The content gets life as the monetization opportunities get multiplied with tagging, whether through advertising rates increasing, more syndication opportunities, licencing or recommendations.
- The relevance of each tag, and in an indirect way, the underlying content, can be estimated based on their visibility and usage. This also provides inputs to the organization for future content creation.
- Metadata can provide the intelligence to base publishing decisions like what and when on. It also becomes easier to repurpose content when the description and organization are predictable and consistent.
oWorkers staff being employees, and not contractors as some competitors choose, there is flexibility in deployment. oWorkers pays local and social taxes for all staff. They regularly receive ratings of 4.65 or more on a scale of 5 on portals like Glassdoor.
Where does AI for custom content tagging fit in?
Content published on the web needs metadata to be associated with it so that it gets context and becomes searchable and accessible to machines and software programs, which is what content tagging does. If content tagging adds value, it needs to be done.
Before automation there was, or is, a manual process. The same applies to content tagging. In the examples of the blogger that we had discussed earlier, she perhaps tags her content manually. This is also how many organizations do it when they start out with producing content for consumption of their customers or others. As they find success with their early efforts, the need for larger volumes of content, and consequently greater tagging effort, soon become a reality, especially for consumer businesses that need to reach out to a large number of customers and potential customers.
It may be enough for some organizations to neatly classify their content for internal use, many others will probably wish to extract the mileage they can extract out of it for business benefit. They would like to use the content at their disposal, which is also growing, in an intelligent manner, again making content tagging a requirement.
Depending on the core system where tagging is being done, content tags can be applied either in a rigid manner, in the form of selecting from an available dropdown list, or it can be a more open system where even users are allowed to create tags that then become available for future use and search. A combination of the two may also be possible.
Manual tagging, while it may deliver results that are closest to what the content owner desires, suffers from the usual limitations of manual processes. It limits the scale to which it can be applied; a human can only do so much and no more. Expanding the manual effort entails a cost. And sometimes, humans can think and act in different ways, thanks to the organ known as the human brain, leading to compromises in standardization.
This is where automation and AI for custom content tagging can overcome some of these limitations and support the business in tagging their content. Technology makes it possible to perform content tagging in many different ways, mostly relying on advances like semantic extraction, content analytics and natural language processing (NLP), creating metadata that enables other machines on the world wide web to find that piece of content. More often than not, AI models are relied upon to make the unstructured information understandable to other computers.
While it may enable content owners to efficiently and effectively tag their content to make it accessible, how they do it and what they do with the content in order to meet the business objectives, remains their problem to solve.
Their deep relationships with technology companies gives oWorkers the heft to leverage the latest technologies and use them for delivery which, again, is a benefit to clients. oWorkers is not only GDPR compliant, but also ISO (27001:2013 & 9001:2015) certified.
What does AI for custom content tagging do?
Many publishers are looking at AI with hopes of leveraging the technology for creating content tagging for their vast archives of information, thereby releasing them for use and monetization. Once the engine has been primed with enough training in the form of terms and examples and samples, and the AI now knows what to expect and what to do, it can speed up the process of publishing and make content available faster. It also makes content tagging predictable and less prone to the errors that only humans can make, like missing put on key tags altogether.
While in publishing and archiving documents, time may not be a constraint, but in certain other aspects of publishing, like news, time is of the essence. At such times, expecting humans to create metadata at the speed of breaking news might be unrealistic. In such situations, an AI engine can keep pace with breaking news and continue to tag content as soon as it is published. Of course, the underlying assumption in this is that the AI engine has been well trained through adequate examples and data.
Individuals will often save content in ways that seem most appropriate to them. But everyone thinks differently. What may be intuitive to one may not be to another. Companies that rely on AI for tagging often improve the searchability of their content manifold, as it becomes predictable and standard. AI can work with form data, extract information from unstructured text and help in grouping similar content together.
With its clearly defined focus on data services, oWorkers is a pure player and has been identified as one of the top three BPO providers in the world. It is led by a team that has over 20years of hands-on experience in the industry.
oWorkers provides tagging support
Organizations are not alone if they are starting out on their journey. Established vendors like oWorkers, who have been doing content tagging for many years, are available for support for AI for custom content tagging.
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