A Comprehensive Guide to Natural Language Generation
As long as Artificial Intelligence helps us to get more out of the natural language, we see more tasks and fields mushrooming at the intersection of AI and linguistics. In one of our previous articles, we discussed the difference between Natural Language Processing and Natural Language Understanding. Both fields, however, have natural languages as input. At the same time, the urge to establish two-way communication with computers has lead to the emergence of a separate subcategory of tasks dealing with producing (quasi)-natural speech. This subcategory, called Natural Language Generation will be the focus of this blog post.
What is NLG?
Natural Language Generation, as defined by Artificial Intelligence: Natural Language Processing Fundamentals, is the “process of producing meaningful phrases and sentences in the form of natural language.” In its essence, it automatically generates narratives that describe, summarize or explain input structured data in a human-like manner at the speed of thousands of pages per second.
However, while NLG software can write, it can’t read. The part of NLP that reads human language and turns its unstructured data into structured data understandable to computers is called Natural Language Understanding.
In general terms, NLG (Natural Language Generation) and NLU (Natural Language Understanding) are subsections of a more general NLP domain that encompasses all software which interprets or produces human language, in either spoken or written form:
- NLU takes up the understanding of the data based on grammar, the context in which it was said and decide on intent and entities.
- NLP converts a text into structured data.
- NLG generates a text based on structured data.
Major applications of NLG
NLG makes data universally understandable making the writing of data-driven financial reports, product descriptions, meeting memos, and more much easier and faster. Ideally, it can take the burden of summarizing the data from…