NLP vs NLU vs NLG: Understanding the Differences by Tathagata Medium

NLP is used to process and analyze large amounts of natural language data, such as text and speech, and extract meaning from it. NLG, on the other hand, is a field of AI that focuses on generating natural language output. Natural Language Understanding (NLU) is a field of NLP that allows computers to understand human language in more than just a grammatical sense. It also means they can comprehend what the speaker or writer is trying to say and its intent. Businesses could use this for customer service applications such as chatbots and virtual assistants. As we continue to advance in the realms of artificial intelligence and machine learning, the importance of NLP and NLU will only grow.

Conversely, NLU focuses on extracting the context and intent, or in other words, what was meant. Natural languages are different from formal or constructed languages, which have a different origin and development path. For example, programming languages including C, Java, Python, and many more were created for a specific reason.

The difference between Natural Language Processing (NLP) and Natural Language Understanding (NLU)

Though NLU understands unstructured data, part of its core function is to convert text into a structured data set that a machine can more easily consume. This also includes turning the  unstructured data – the plain language query –  into structured data that can be used to query the data set. It uses neural networks and advanced algorithms to learn from large amounts of data, allowing systems to comprehend and interpret language more effectively. NLU often involves incorporating external knowledge sources, such as ontologies, knowledge graphs, or commonsense databases, to enhance understanding.

It also facilitates sentiment analysis, which involves determining the sentiment or emotion expressed in a piece of text, and information retrieval, where machines retrieve relevant information based on user queries. NLP has the potential to revolutionize industries such as healthcare, customer service, information retrieval, and language education, among others. NLP takes input text in the form of natural language, converts it into a computer language, processes it, and returns the information as a response in a natural language. NLU converts input text or speech into structured data and helps extract facts from this input data. NLP consists of natural language generation (NLG) concepts and natural language understanding (NLU) to achieve human-like language processing. Until recently, the idea of a computer that can understand ordinary languages and hold a conversation with a human had seemed like science fiction.

Key Components of NLP, NLU, and NLG

A natural language is a language used as a native tongue by a group of speakers, such as English, Spanish, Mandarin, etc. Simply put, you can think of ASR as a speech recognition software that lets someone make a voice request. The transcription uses algorithms called Automatic Speech Recognition (ASR), which generates a written version of the conversation in real time. NLU is also able to recognize entities, i.e. words and expressions are recognized in the user’s request (input) and can determine the path of the conversation.

This has implications for various industries, including journalism, marketing, and e-commerce. As customers browse or search your site, dynamic recommendations encourage customers to … Like other modern phenomena such as social nlp vs nlu media, artificial intelligence has landed on the ecommerce industry scene with a giant … We’ll also examine when prioritizing one capability over the other is more beneficial for businesses depending on specific use cases.