NLP vs NLU vs. NLG: the differences between three natural language processing concepts

With NLP integrated into an IVR, it becomes a voice bot solution as opposed to a strict, scripted IVR solution. Voice bots allow direct, contextual interaction with the computer software via NLP technology, allowing the Voice bot to understand and respond with a relevant answer to a non-scripted question. Natural Language Processing allows an IVR solution to understand callers, detect emotion and identify keywords in order to fully capture their intent and respond accordingly.

• Text analysis solutions enable machines to automatically understand the content of customer support tickets and route them to the correct departments without employees having to open every single ticket.
• In the world of AI, for a machine to be considered intelligent, it must pass the Turing Test.
• Using a set of linguistic guidelines coded into the platform that use human grammatical structures.
• We discussed this with Arman van Lieshout, Product Manager at CM.com, for our Conversational AI solution.
• Natural language processing has made inroads for applications to support human productivity in service and ecommerce, but this has largely been made possible by narrowing the scope of the application.
• People can express the same idea in different ways, but sometimes they make mistakes when speaking or writing.

Natural language understanding helps decipher the meaning of users’ words (even with their quirks and mistakes!) and remembers what has been said to maintain context and continuity. As a result, algorithms search for associations and correlations to infer what the sentence’s most likely meaning is rather than understanding the genuine meaning of human languages. Natural language processing is generally more suitable for tasks involving data extraction, text summarization, and machine translation, among others.

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Think of the classical example of a meaningless yet grammatical sentence “colorless green ideas sleep furiously”. Even more, in the real life, meaningful sentences often contain minor errors and can be classified as ungrammatical. Human interaction allows for errors in the produced text and speech compensating them by excellent pattern recognition and drawing additional information from the context. This shows the lopsidedness of the syntax-focused analysis and the need for a closer focus on multilevel semantics. Natural language understanding is the first step in many processes, such as categorizing text, gathering news, archiving individual pieces of text, and, on a larger scale, analyzing content. Real-world examples of NLU range from small tasks like issuing short commands based on comprehending text to some small degree, like rerouting an email to the right person based on a basic syntax and decently-sized lexicon.

Natural Language Generation(NLG) is a sub-component of Natural language processing that helps in generating the output in a natural language based on the input provided by the user. This component responds to the user in the same language in which the input was provided say the user asks something in English then the system will return the output in English. However, the full potential of NLP cannot be realized without the support of NLU. And so, understanding NLU is the second step toward enhancing the accuracy and efficiency of your speech recognition and language translation systems.

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The best NLP solutions follow 5 NLP processing steps to analyze written and spoken language. Understand these NLP steps to use NLP in your text and voice applications effectively. Human language is typically difficult for computers to grasp, as it’s filled with complex, subtle and ever-changing meanings.

SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items. Our open source conversational AI platform includes NLU, and you can customize your pipeline in a modular way to extend the built-in functionality of Rasa’s NLU models. You can learn more about custom NLU components in the developer documentation, and be sure to check out this detailed tutorial. The procedure of determining mortgage rates is comparable to that of determining insurance risk. As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data. However, NLU lets computers understand “emotions” and “real meanings” of the sentences.

Scope and context

While both understand human language, NLU communicates with untrained individuals to learn and understand their intent. In addition to understanding words and interpreting meaning, NLU is programmed to understand meaning, despite common human errors, such as mispronunciations or transposed letters and words. NLP attempts to analyze and understand the text of a given document, and NLU makes it possible to carry out a dialogue with a computer using natural language. Question answering is a subfield of NLP and speech recognition that uses NLU to help computers automatically understand natural language questions. Before a computer can process unstructured text into a machine-readable format, first machines need to understand the peculiarities of the human language. Grammar and the literal meaning of words pretty much go out the window whenever we speak.

Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions. By combining their strengths, businesses can create more human-like interactions and deliver personalized experiences that cater to their customers’ diverse needs. This integration of language technologies is driving innovation and improving user experiences across various industries.

Natural Language Processing

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 nlu and nlp understandable part of a word). With the advent of ChatGPT, it feels like we’re venturing into a whole new world. Everyone can ask questions and give commands to what is perceived as an “omniscient” chatbot.

Recent groundbreaking tools such as ChatGPT use NLP to store information and provide detailed answers. 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.

If NLP is about understanding the state of the game, NLU is about strategically applying that information to win the game. Thinking dozens of moves ahead is only possible after determining the ground rules and the context. Working together, these two techniques are what makes a conversational AI system a reality.

The verb that precedes it, swimming, provides additional context to the reader, allowing us to conclude that we are referring to the flow of water in the ocean. The noun it describes, version, denotes multiple iterations of a report, enabling us to determine that we are referring to the most up-to-date status of a file. And also the intents and entity change based on the previous chats check out below. It is quite common to confuse specific terms in this fast-moving field of Machine Learning and Artificial Intelligence. The above is the same case where the three words are interchanged as pleased.

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. NLP is an umbrella term which encompasses any and everything related to making machines able to process natural language—be it receiving the input, understanding the input, or generating a response.