Natural Language Generation (NLG) is a subdivision of Artificial Intelligence (AI) that aims to reduce communicative gaps between machines and humans. The technology, typically accepts input in non-linguistic format and turn it into human understandable formats like reports, documents, text messages etc.
The entire process, however, is complex and involves a number of criterias to render an output. This includes a goal, a situation, a context, a model of person for whom the output is produced and therefore, the length of output varies from a word, to line, to a paragraph.
Natural Language Processing is one of the AI technologies that will be in forefront in 2018. It has got amazing potential to make intelligent software applications. In the later segment, we discuss some of the ways NLG can simplify some of the complex tasks by transforming machine representation into natural language.
Natural Language Processing: Applications and Examples
There are two ways NLG can find its use-cases in software applications. Either to present a set of information to the users or to automate mass production of chore documents (creating reports, sending text messages etc.).
- Converting Tabular Insights into Narratives (Interpretation)
The finance market oscillates a lot. While the analysts can easily read out stock analysis report, equity reports, stress test report etc. and come to a conclusion, it is perplexing for layman to understand them. In order to make users to comprehend the output, the software applications in finance industries are making the most of Natural Language Processing.
- Numerical Weather Reports into Text (Automation)
Most of the weather forecasting systems use Natural Language Processing to interpret the numerical values that are received as an input from supercomputers. Just take example of how fog is reported. A numerical prediction of wind speed, intensity of precipitation, and other meteorological phenomena are recorded. For converting this data into a language (text, audio, print, or any other form) that humans understand, Natural Language Generation can be used. The system will continue to receive the values and NLG interprets them into human understandable format.
Different Variations of NLG:
- Basic NLG: A simplified form of Natural Language Processing, this will allow translating data into text (through Excel-like functions). To relate, take example of MS Word mailmerge, wherein a gap is filled with some data, which is retrieved from another source (say a table in MS Excel).
- Templated NLG: This form of NLG uses template driven mode to display the output. Take example of the football match score board. The data keeps changes dynamically and is generated by predefined set of business rules like if/else loop statements.
- Advanced NLG: This form of Natural Language Generation communicates just like humans. It understands the intent, add intelligence, consider context, and render the result in insightful narratives that users can easily read and comprehend.
Software Applications Powered by NLG:
Now that you have a clue about how NLG and other AI technologies can add value to your software applications, it’s the time to make the most out of them. NLG has its future scope in conversational interfaces, virtual assistants, software applications (like weather forecasting, news writing, canned text generation etc.).
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