What Are Different Attributes of NLG Processing?

Natural Language Generation is a software process that automatically converts data into written text. This process is utilized for the fast generation of business intelligence for the market. As more and more organizations seek to equip themselves with relevant NLG tools, the global NLG market is expected to reach US$825.3 million in 2023 up from US$322.1 million in 2018.

According to a market report, the drivers behind this demand are clear. Organizations face increasingly complex challenges every day, competition is fierce and the effect is that profits are harder to generate than ever before. Meanwhile, increasing regulation and transparency requirements are an ever-growing burden. The organizations already have the data they need to overcome these challenges, but converting it into intelligence that can support informed decision-making ties up their data experts or quants with routine and repetitive tasks. Given the exponential growth of available data, surfacing the most relevant insights is the most worthwhile goal. NLG can automatically turn this data into human-friendly prose.

Let's understand more about NLG

Natural Language Generation (NLG) is the process of producing phrases, sentences, and paragraphs that are meaningful from an internal representation. It is a part of Natural Language Processing and happens in four phases: identifying the goals, planning on how goals may be achieved by evaluating the situation and available communicative sources and realizing the plans as a text. It is the opposite of Understanding.

The process of language generation involves the following interweaved tasks.

Content selection: Information should be selected and included in the set. Depending on how this information is parsed into representational units, parts of the units may have to be removed while some others may be added by default.

Textual Organization: The information must be textually organized according to the grammar, it must be ordered both sequentially and in terms of linguistic relations like modifications.

Linguistic Resources: To support the information's realization, linguistic resources must be chosen. In the end, these resources will come down to choices of particular words, idioms, syntactic constructs, etc.

Realization: The selected and organized resources must be realized as an actual text or voice output.

Different Variations of NLG

Basic NLG: It is a simplified form of Natural Language Processing, which will allow translating data into text (through Excel-like functions). To relate, take the 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 the example of the football match scoreboard. The data keeps change dynamically and is generated by a 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, adds intelligence, considers context, and render the result in insightful narratives that users can easily read and comprehend.