Natural Language Generation
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Natural Language Generation
Natural Language Generation (NLP) is one of the most widely used Artificial Intelligence applications that specifically producing the text from analyzing the data from the computer. Simply, it has been served as a translator which allows converting the computerized data into the natural language representation like plain-English. According to the collected data and input has given by a user, it will provide a conclusion including data in the form of text (Kendall, 2019).
Natural Language Generation is exactly termed as contrary to Natural Language Understanding. In the process of natural language understanding, the system has to disambiguate the input data into the machine representation language. Whereas Natural Language Generation is allowing to make decisions regarding how to convert the machine representation language into the text.
The process of producing general text by NLP is simpler due to the generation of a list of readymade text that can be copied and pasted. It can be acceptable in some of the applications such as horoscope machines or generators of personalized business letters. But, some of the applications require natural language that means the text should not be repetitive so the NLP system should be sophisticated to subsume the stages of planning and merging of information.
For example, a simple NLG system could have been used in the Pollen Forecast for Scotland system that assists to provide the prediction of pollen levels in different parts of Scotland by analyzing the input data as six numbers. Relevantly, the AI device with the NLG system produces the output text including Grass Pollen levels for Friday have reached the high levels from a moderate state that included the values from 6 to 7 across most parts of the country. In Northern areas of Scotland, the Pollen levels will be moderate with the values of 4 (Insights, 2018).
Typical Stages of Natural Language Generation System
Content Determination:
Initially, the Natural Language Generation system has been focused on the main content to be represented in a sentence or the information to mention in the text (Makadia, 2018). Let’s say, it is predicting whether to mention the pollen level is 7 in the south-east for the above-mentioned example.
Document Structuring:
It will make a decision regarding the organization or structure of the conveyed data.
Aggregation:
In order to improve understanding and readability, the NLG system allows putting similar sentences together.
Lexical Choice:
To provide the information correctly, it will use the appropriate words that should convey the meaning to the users.
Referring Expression Generation:
It is also creating referral expression in the generation of text that helpful for the identification of a particular region or area.
Realization:
Finally, it will check out whether the information is valid including grammar errors.