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Automatically Obtain Natural Language Generation from Structured Data

After the exponential increase in the amount of data generated from various aspects of digital applications, it has become necessary to rely on NLG applications to get the maximum benefit from these tons of data.

The main idea of Natural Language Generation from Structured Data is to process any form of organized and abstract data and find out what is useful from it, then convert it into a human-readable and understandable language so that an ordinary person can realize and totally benefit from it.

natural language generation from structured data

For example, if we have a data table for a football league, including: team names and standings, league top scorer, current and upcoming matches, etc…

We will be able to automatically generate a bulletin that explains this data to the user in a friendly and clear way. It is even possible to customize this report to suit different disciplines, i.e. generate a bulletin that contains attractive information and texts that are suitable for sports commentators, or create another one that contains formatted information that is suitable for sports program producers.

The process of Natural Language Generation from Structured Data goes through several stages, as follows:

  • Identify and understand the content to be generated.
  • Develop outline and the template of the content.
  • Building and aggregating sentences.
  • Entering synonyms.
  • Check spelling and grammar.
  • Generate the final text.
nlg from structured data

The previous steps are often performed in the same order. In this article you will find a summary of everything related to Natural Language Generation from Structured Data.

Before you Start

It is important to realize the concept of Natural Language Generation from Structured Data comes from many sources, such as specifications of electronic devices, warehouse information and invoices, etc.

If we leave it entirely for the machine to convert the data into readable texts without the use of NLG applications, we will get a poor results, because the computer will not fully understand the intent of data or what is important from it, therefore it will treat it as a series of words and convert it to another understandable string.

Here comes the role of the language model, it is the place where man interacts and sets the necessary rules to generate the natural text, in terms of writing sentences, arranging them in logical paragraphs, and setting the appropriate circumstances (if conditions) that later lead to NLG from Structured Data.

It’s like we’re teaching the machine how to understand the context and respond the way we want it to.

Identify and understand the content

This stage depends on choosing what you want to include in the resulting content and thus knowing what columns of data to use from the overall data source. Several factors play an important role in this choice. Here are the most important points:

  • The purpose of the resulting content. Does the reader need to be given educational information or needs to have his point of view changed?
  • The target audience. Determining the audience who will receive the resulting content, are they academics – students – professionals.

Develop outline and the template of the content

At this stage, you will create the structure of the story (list or paragraphs). This structure may depend on how the relationship between the data is shown or depend on listing the important information first and then grading to the least important.

Building and aggregating sentences

To get readable Natural Language Generation from Structured Data we need to consider writing sentences coherently, aggregating information that gives the same concept in one sentence, in addition to eliminating extra words or sentences and setting up the needed conditions that keep compatibility with data intent.

natural language generation from structured data

Entering synonyms

Now is the time for human Interaction in order to set synonyms for words and sentences, which leads to the text being synthesized in different language models to choose the best one, which is completely depend on the experience that the user of the NLG application has, and the built in functionality in the application.

The task of this step is not limited to choosing the best in the abstract, but also extends to the ability of those synonyms to accurately express what is meant by the sentence. For example, do I use the word “Easy” , “Simple”  or “Not Difficult”? Which one gives the meaning to be achieved? To solve this issue, it is possible to rely on prior knowledge of the target domain in an attempt to build several possible combinations that ultimately lead to the best possible targeting.

Check spelling and grammar

To gain a very good Natural Language Generation from Structured Data , you must check the spelling and grammar of the resulting text and make sure that the appropriate morphological forms are used, in addition to the correct use of punctuation and prepositions to obtain a smooth and clear text. You can rely on the applications of AI related to this field.

Generate the final text

It is the final stage, in which we test several variations of the resulting text, and then generate the final text that is valid for publication and sharing. The specifics of this step depend on the functionality of the NLG application being used and on the quality requirements to be achieved in the final results.

Also read our guide on What is Natural Language Generation

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