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Natural Language Generation |
Natural Language Generation (NLG) is an artificial intelligence discipline that turns input into plain English material automatically. It may be used to create long-form material for businesses, as well as specialised reports and content for mobile applications. Natural language generation (NLG) differs from natural language processing (NLP) in that NLP is used to produce textual data by combining analytic output with contextualised narratives, whereas NLG is used to generate textual data by combining analytic output with contextualised narratives. Data may be retrieved, analysed, and transmitted with scale and precision using NLG.
Automated
Natural Language Generation is similar to the mechanism individuals use to convert their thoughts into writing or voice. This process, which may also be defined mathematically or recreated on a computer for psychological study, is referred to by psycholinguists as language production. NLG systems are similar to artificial computer language translators such as decompilers and transpilers, which generate human-readable code from an intermediate representation. Human languages are far more complex than computer languages, allowing for far more ambiguity and variation of expression, making NLG more difficult.
How NLG worksNLG is a multi-stage process that refines the data utilised to create content using natural-sounding language at each level. The following are the six stages of NLG:
Content analysis: Data is filtered to identify what should be included in the final content. Identifying the key subjects in the source material, as well as the links between them, is part of this step.
Data understanding: The data is analysed, patterns are discovered, and the information is placed in context. At this point, machine learning is frequently applied.
Document structuring: Based on the sort of data being analysed, a document plan is constructed and a narrative framework is established.
Sentence aggregation: Sentences or sections of sentences that are relevant to the issue are mixed in ways that correctly describe the topic.
Grammatical structuring: To create natural-sounding writing, grammatical rules are employed. The sentence's syntactical structure is deduced by the software. It then rewrites the statement in a grammatically accurate manner using this information.
Language presentation: The final output is created using a template or format chosen by the user or programmer.
In marketing analytics, machine learning, and sales analytics, natural language creation is becoming more popular. Insights into the market may be gained through sales and marketing analytics. NLG enables analytics technologies to produce replies that are simple and intelligible right away.
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