What are the Differences Between NLP, NLU, and NLG?

It’s also changing how users discover content, from what they search for on Google to what they binge-watch on Netflix. It is easy to confuse common terminology in the fast-moving world of machine learning. For example, the term NLU is often believed to be interchangeable with the term NLP.

Its prime objective is to bring out the actual intent of the speaker. Textual entailment (shows direct relationship between text fragments) is a part of NLU. NLU smoothens the process of human machine interaction; it bridges the gap between data processing and data analysis. NLP has many subfields, including computational linguistics, syntax analysis, speech recognition, machine translation, and more. NLP is a critical piece of any human-facing artificial intelligence.

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Conversational interfaces are powered primarily by natural language processing (NLP), and a key subset of NLP is natural language understanding (NLU). The terms NLP and NLU are often used interchangeably, but they have slightly different meanings. Developers need to understand the difference between natural language processing and natural language understanding so they can build successful conversational applications. In machine learning (ML) jargon, the series of steps taken are called data pre-processing. The idea is to break down the natural language text into smaller and more manageable chunks. These can then be analyzed by ML algorithms to find relations, dependencies, and context among various chunks.

So, when building any program that works on your language data, it’s important to choose the right AI approach. This is in contrast to NLU, which applies grammar rules (among other techniques) to “understand” the meaning conveyed in the text. 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. In the age of conversational commerce, such a task is done by sales chatbots that understand user intent and help customers to discover a suitable product for them (see Figure 6).

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However, because language and grammar rules can be complex and contradictory, this algorithmic approach can sometimes produce incorrect results without human oversight and correction. In this journey, we’ll learn about NLP and NLU, how they can help your business in today’s data-driven world, and the challenges businesses might face if they don’t use these technologies in their apps or systems. NLP is a fast-growing study subject in AI, with applications such as translation, summarization, text production, and sentiment analysis. Businesses utilize NLP to fuel an increasing number of applications, both internal and customer-facing, such as detecting insurance fraud, evaluating customer sentiment, and optimising aircraft maintenance.

This is due to the fact that with so many customers from all over the world, there is also a diverse range of languages. At this point, there comes the requirement of something called ‘natural language’ in the world of artificial intelligence. John Snow Labs NLU provides state of the art algorithms for NLP&NLU with 20000+ of pretrained models in 200+ languages. It enables swift and simple development and research with its powerful Pythonic and Keras inspired API.

The Future of Large Language Models

Let’s illustrate this example by using a famous NLP model called Google Translate. As seen in Figure 3, Google translates the Turkish proverb “Damlaya damlaya göl olur.” as “Drop by drop, it becomes a lake.” This is an exact word by word translation of the sentence. Natural language generation (NLG) is the construction of text in English or other languages by a machine using a given dataset. Alan Turing pioneered it in order for a machine to understand the context of any document rather than simply treating it as a collection of words. They can predict which words should be generated next (for example, in an email you’re writing).

The program breaks language down into digestible bits that are easier to understand. These terms are often confused because they’re all part of the singular process of reproducing human communication in computers. See how easy it is to use any of the thousands of models in 1 line of code, there are hundreds of tutorials and simple examples you can copy and paste into your projects to achieve State Of The Art easily. John Snow Labs’ NLU is a Python library for applying state-of-the-art text mining, directly on any dataframe, with a single line of code. As a facade of the award-winning Spark NLP library, it comes with 1000+ of pretrained models in 100+, all production-grade, scalable, and trainable, with everything in 1 line of code. Furthermore, scientific research on NLP has produced mixed results.

Does NLP work?

2 min read – By acquiring Apptio Inc., IBM has empowered clients to unlock additional value through the seamless integration of Apptio and IBM. NLG also encompasses text summarization capabilities that generate summaries from in-put documents while maintaining the integrity of the information. Extractive summarization is the AI innovation powering Key Point Analysis used in That’s Debatable.

Going back to our weather enquiry example, it is NLU which enables the machine to understand that those three different questions have the same underlying weather forecast query. After all, different sentences can mean the same thing, and, vice versa, the same words can mean different things depending on how they are used. But before any of this natural language processing can happen, the text needs to be standardized.

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