Natural Language Generation (NLG) is the process of converting data into human-readable text. This is done by using algorithms to generate sentences from scratch, or by applying rules to existing text.
NLG is used in a variety of applications, such as chatbots, digital assistants, and automatic summarization. It can be used to generate reports, descriptions, and explanations.
NLG is sometimes confused with Natural Language Processing (NLP). However, NLP deals with understanding and manipulating human language, while NLG deals with generating it.
In the text analytics industry, Natural Language Generation is used to create summaries of data sets too large for humans to process. It can also be used to generate reports on a regular basis, or to create real-time alerts about changes in data.
Natural Language Generation can be used outside of the text analytics industry as well. For example, it can be used to generate descriptions of products on e-commerce websites, or to create automatic captions for videos.
NLG vs Semantic Analysis
Natural Language Generation should not be confused with semantic analysis, which is the process of extracting meaning from text. Semantic analysis can be used to understand the sentiment of a text, or to automatically categorize it. However, it cannot generate new text from scratch.
Can NLG Replace Humans ?
Natural Language Generation can create human-readable text from data, but it cannot replace humans altogether. It is often used to generate summaries or reports that would be too time-consuming for humans to create, but it cannot generate original content or have a conversation.
Natural Language Generation has a number of limitations. First, it is difficult to control the tone of the generated text. Second, NLG can sometimes create text that is hard to understand, or that contains grammatical errors. Finally, NLG depends on data that is structured in a certain way, which can be a challenge for some organizations.