4 Differences between NLP and NLU

The key aim of any Natural Language Understanding-based tool is to respond appropriately to the input in a way that the user will understand. The voice assistant uses the framework of Natural Language Processing to understand what is being said, and it uses Natural Language Generation to respond in a human-like manner. There is Natural Language Understanding at work as well, helping the voice assistant to judge the intention of the question. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission.

The rest 80% is unstructured data, which can’t be used to make predictions or develop algorithms. A common example of this is sentiment analysis, which uses both NLP and NLU algorithms in order to determine the emotional meaning behind a text. NLP has many subfields, including computational linguistics, syntax analysis, speech recognition, machine translation, and more. That’s why companies are using natural language processing to extract information from text.

Defining NLU (Natural Language Understanding)

Part of this care is not only being able to adequately meet expectations for customer experience, but to provide a personalized experience. Accenture reports that 91% of consumers say they are more likely to shop with companies that provide offers and recommendations that are relevant to them specifically. This is achieved by the training and continuous learning capabilities of the NLU solution.

For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. NLP or natural language processing is evolved from computational linguistics, which aims to model natural human language data. On our quest to make more robust autonomous machines, it is imperative that we are able to not only process the input in the form of natural language, but also understand the meaning and context—that’s the value of NLU. This enables machines to produce more accurate and appropriate responses during interactions. 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.

Learn

Natural Language Understanding deconstructs human speech using trained algorithms until it forms a structured ontology, or a set of concepts and categories that have established relationships with one another. This computational linguistics data model is then applied to text or speech as in the example above, first identifying key parts of the language. NLU helps computers to understand human language by understanding, analyzing and interpreting basic speech parts, separately. 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. Not only does this save customer support teams hundreds of hours,it also helps them prioritize urgent tickets. Natural language understanding is a subfield of natural language processing.

NLU tools should be able to tag and categorize the text they encounter appropriately. Of course, Natural Language Understanding can only function well if the algorithms and machine learning that form its backbone have been adequately trained, with a significant database of information provided for it to refer to. Without sophisticated software, understanding implicit factors is difficult.

Sign language

The major difference between the NLU and NLP is that NLP focuses on building algorithms to recognize and understand natural language, while NLU focuses on the meaning of a sentence. Furthermore, NLU and NLG are parts of NLP that are becoming increasingly important. These technologies use machine learning to determine the meaning of the text, which can be used in many ways. Artificial intelligence is becoming an increasingly important part of our lives. However, when it comes to understanding human language, technology still isn’t at the point where it can give us all the answers. In addition to processing natural language similarly to a human, NLG-trained machines are now able to generate new natural language text—as if written by another human.

Try out no-code text analysis tools like MonkeyLearn to  automatically tag your customer service tickets. Simply put, using previously gathered and analyzed information, computer programs are able to generate conclusions. For example, in medicine, machines can infer a diagnosis based on previous diagnoses using IF-THEN deduction rules. NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans.

Solution Type

And if we decide to code rules for each and every combination of words in any natural language to help a machine understand, then things will get very complicated very quickly. Two key concepts in natural language processing are intent recognition and entity recognition. NLU, a subset of natural language processing (NLP) and conversational AI, helps conversational AI applications to determine the purpose of the user and direct them to the relevant solutions. For example, after training, the machine can identify “help me recommend a nearby restaurant”, which is not an expression of the intention of “booking a ticket”. Techniques for NLU include the use of common syntax and grammatical rules to enable a computer to understand the meaning and context of natural human language. 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.

NLG systems enable computers to automatically generate natural language text, mimicking the way humans naturally communicate — a departure from traditional computer-generated text. A basic form of NLU is called parsing, which takes written text and converts it into a structured format for computers to understand. Instead of relying on computer language syntax, NLU enables a computer to comprehend and respond to human-written text. Depending on your business, you may need to process data in a number of languages. Having support for many languages other than English will help you be more effective at meeting customer expectations.

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