NLP, NLU, and NLG: The World of a Difference

By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly. Sometimes people know what they are looking for but do not know the exact name of the good. In such cases, salespeople in the physical stores used to solve our problem and recommended us a suitable product.

NLU is used along with search technology to better answer our most burning questions. In traditional Natural Language techniques, the question is pulled into a graph structure that deconstructs the sentence the way you did in elementary school. I deliberately bolded the word ‘understand’ in the previous section because that part is the one which is specifically called NLU.

Machine Learning

So NLU is a subset of NLP where semantics of the input text are identified and made use of, to draw out conclusions ; which means that NLP without NLU would not involve meaning of text. Recent advances in AI technology have allowed for a more detailed comparison of the two algorithms. A number of studies have been conducted to compare the performance of NLU and NLP algorithms on various tasks. One such study, conducted by researchers from the University of California, compared the performance of an NLU algorithm and an NLP algorithm on the task of question-answering. The results showed that the NLU algorithm outperformed the NLP algorithm, achieving a higher accuracy rate on the task.

Sometimes you may have too many lines of text data, and you have time scarcity to handle all that data. NLG is used to generate a semantic understanding of the original document and create a summary through text abstraction or text extraction. In text extraction, pieces of text are extracted from the original document and put together into a shorter version while maintaining the same information content. Text abstraction, the original document is phrased in a linguistic way, text interpreted and described using new concepts, but the same information content is maintained. Accurately translating text or speech from one language to another is one of the toughest challenges of natural language processing and natural language understanding. With text analysis solutions like MonkeyLearn, machines can understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket.

Difference between NLU vs NLP applications

Statistical approaches are data-driven and can handle more complex patterns. It’s taking the slangy, figurative way we talk every day and understanding what we truly mean. Semantically, it looks for the true meaning behind the words by comparing them to similar examples. At the same time, it breaks down text into parts of speech, sentence structure, and morphemes (the smallest understandable part of a word).

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