Natural Language Generation (NLG) is a form of artificial intelligence that generates natural language from structured data. NLG is a software process that automatically transforms data into plain-English content. The content is written as a narrative by the technology, replete with sentences and paragraphs.
Understanding Natural Language Generation
Natural Language Generation is a rapidly growing field that has seen great popularity among businesses. While NLG has an endless array of applications, it is useful for time or resource-intensive activities where is a need to generate content from data at scale.
Such applications include:
- Written analysis for business intelligence dashboards.
- App or email-based customer communication.
- Client portfolio updates and summaries.
- Landing page content and eCommerce product descriptions.
- Internet of things (IoT) device maintenance and status reporting.
Four processes of Natural Language Generation architecture
NLG could never replicate the text generated by a real person, but it does use a range of methods to adapt its writing style according to the tone, structure, context, and purpose of the narrative.
To clarify these methods, researchers must define these processes:
- Document planning – to determine what should be said, an abstract document is created based on the knowledge of the user. Information must also consider the goals of both the writer and reader.
- Sentence planning – what are the referring expressions or word choices? How will the sentences and paragraphs be structured? This step is sometimes called microplanning and involves techniques such as referring expressions, aggregation, grammaticalization, and lexicalization.
- Surface realization – or the generation of grammatically correct sentences using proper syntax and inflection.
- Physical presentation – depending on whether the information is written or spoken, the text must contain the right articulation, layout, or punctuation.
Natural Language Generation and Natural Language Processing
Natural Language Generation can write information, but it cannot read it.
This is where Natural Language Processing (NLP) comes in. NLP systems can “read” information in the sense that they can look at human language and determine what ideas are being communicated. Note that ideas are not communicated by words alone. Context, body language, and intonation are also vital in gauging the intent of the spoken word.
In this way, NLP systems incorporate ideas from computer science and computational linguistics to bridge the gap between nuanced human communication and computer understanding.
Real-world applications of NLP
Many of us encounter NLP during our lives without realizing it. Here are some of the more interesting applications:
- Virtual assistants such as Siri, Amazon Echo, and Google Home.
- Email assistants that correct grammar or filters determine which emails are likely to be spam and which should be sent to the inbox.
- Chatbots that answer customer service inquiries on eCommerce sites in real-time.
- Aircraft maintenance, where NLP is being used to find meanings in the verbal and written descriptions of aircraft problems given by pilots.
- Predictive police work. Although in its infancy, NLP is being used to assist detectives in determining the motives for crimes based on the language of the offender.
- Natural Language Generation is a type of artificial intelligence that generates natural language from structured data.
- Natural Language Generation uses four key processes to reconstruct the context, tone, structure, and purpose of a narrative or story. These processes are document planning, sentence planning, surface realization, and physical presentation.
- While Natural Language Generation can write information, Natural Language Processing can read it. Through detailed analysis of written and verbal information, NLP has several interesting and important applications.
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