Natural Language Generation (NLG) is the process of using artificial intelligence to generate language for various purposes.
NLG systems are used in a variety of contexts, such as chatbots designed for customer service or automated question answering services like Siri and Alexa.
While these systems are not yet capable of mimicking human speech without conceptional guidelines provided by a user or programmer, they can produce text and sentences which sound natural.
Most systems have underlying deep learning models to learn NLG techniques, such as:
These are the most commonly used techniques.
In most cases, a single NLG technique is not sufficient to generate language. The following image illustrates how we can combine text structuring, lexicalization and linguistic expression generation to create a whole sentence:
This example shows how text linguistic realization allows software to create text outputs from a JSON (data-interchange format) document.
In this case, text structuring places the adjective behind the noun instead of leading to a sentence such as "This is not a cool woman."
Finally, lexicalization is responsible for the correct flection of the determiner (which would be "These" for plural cases) and the verb.
Now, imagine if we switched the underlying data. We could switch up tenses, the amount of nouns and adjectives and much more, while the generated text would still be grammatically correct due to our NLG techniques.
Possible outputs might be:
The advantage: grammatically correct text generations.
The prerequisite: available structured data.
NLG can be used in many different ways. Based on our experience, the most business value can be generated by using NLG as:
There are many reasons to use NLG instead of copywriting resources.
You see, the age of manual content writing is over - at least when it comes to creating thousands of similar, yet unique texts.
What do we mean by "similar"?
Texts that are "similar" are structured and written using similar logic.
Manual content writing en masse is a very tedious process. Let's face it - it's not pleasurable at all, neither for employees nor for contracted content writers.
Our goal with NLG is to leverage the creativity and professionalism of content writers to design great text concepts. These concepts can then be implemented as text models to generate incredible amounts of content.
Instead of creating single texts manually we can now create a concept for certain categories of texts, such a product families, identical types of reports, or texts with similar sentence and paragraph structures.
This is a colossal gain when it comes to scalable and extraordinarily fast generation of relevant content.
Who cares if you can use run-of-the-mill AI content writing software like Jarvis AI to generate cheap text results - if the results are not personalized, are missing relevant information or are even wrong?
With our software solution we enable true scalability because you can:
And the best part?
You don't have to audit every single result since you - not a fully automated AI - control your text logic and structure.
At the end of the day, you need a high performing assistant to generate your ideas within seconds - not a software that thinks that it does your job better than you.
Here the most common application areas are automated product descriptions, category descriptions and meta titles. Every part of a product page that is customized by a content management system (CMS) can be filled with automated texts.
This has many advantages, starting with obvious ones like faster text creation for new products, optimizing for keywords and avoiding duplicate texts.
But a particularly exciting one? Addressing the customer through a targeted user journey and personalized content leads to an average of 10% higher conversions.
Report automation is the process of using software to generate reports based on data inputs. It's most often used in finance, banking and pharma fields where large volumes of reporting are required.
Using natural language generation, each report can be uniquely written with quality that is the same or even better than that of hand written reports.
Those who use report automation enjoy cost savings, increased accuracy and efficiency, and reduced time spent generating reports.
Our NLG software generates reports that garner a lot of readership, for example reports on company valuations or clinical studies.
In the last few years, with advances in artificial intelligence and machine learning, we've seen an increase in the amount of automated sports coverage.
This can be expanded to weather reports, pollen count reports, crime reports, horoscopes, etc.
Currently, highly automatic AI-based generation is not suitable for commercial scenarios - here, input is usually high-volume and heterogenous.
Therefore, rule-based systems or hybrid systems that combine rules and NLG techniques currently prevail in both quality and scalability because:
Future developments might look at more situated language generation where environment, style and personality impact the generated text. This would enable chatbots to adapt to certain slang or a tone of voice that is suitable for their human vis-a-vis. Researches are also pouring a lot of focus into social media research where non-canonical language examples (slang, irony, etc.) are widely available. Non-canonical language poses an even bigger challenge when generating natural language.
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