Methods Employed in Natural Language Generation
The process of Natural Language Generation (NLG) incorporates a variety of methods to convert data into text that is easily understood by humans, utilizing the power of artificial intelligence. Here are some commonly used methods:
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Markov Chains: This language generation technique constructs a language model using n-grams from a sample text, facilitating the creation of new textual content.
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Extractive Approach: This method involves extracting significant sentences from an extensive body of text to form a concise summary.
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Abstractive Approach: This approach pinpoints crucial concepts and generates fresh language to produce unique textual content.
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Sentiment Analysis: This involves scrutinizing the text to ascertain if it’s positive, neutral, or negative, thereby offering actionable insights.
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Text Summarization: This simplifies complex terminologies into more straightforward terms for enhanced comprehension.
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Topic Modeling: This employs AI software to highlight important words and information clusters within the text.
These methods are integral in various applications such as chatbots, voice assistants, content creation, and data analysis. They improve customer interactions and streamline business operations.
