Natural language generation (NLG) is a machine process that transforms structured data into natural language. It can be used to generate automated responses and prompts (e.g. a chatbot) or longer content for websites and mobile applications.
Also called "language production", the process analyzes short input such as keywords or codes and creates content that follows patterns of the natural language and can closely resemble human speech. One of its applications that is becoming particularly popular is automated generation of reports, when data is analyzed to identify trends and the output is a naturally sounding text that describes those trends in comparative terms.
Jelani Harper writing for Inside Big Data:
The foundation of creditable NLG solutions hinges on computational linguistics focused on the science of language itself. By relying on various static AI algorithms and models, this facet of NLG is the enabler of the language analytics component allowing NLG to transmute data (or numbers) into fluent natural language. The most popular NLG deployments provide summaries — “narratives” — of the significance of data of almost any variety.
In this process, computational linguistics support “an entire morphology of the English language and how to use language in sentence form in a way that is very natural,” explained Sharon Daniels, CEO of Arria NLG. With computational linguistics, NLG systems can state the same thing using different terms, distinguish antecedents for pronouns, understand where to punctuate a sentence, and provide other functions traditionally associated with NLP. Moreover, they accomplish this objective with a degree of proficiency so that “you cannot decipher if it was written by an expert or written by…NLG,” Daniels added.
Read more on Inside Big Data.
