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

In NLU, they are used to identify words or phrases in a given text and assign meaning to them. Natural language understanding (NLU) technology plays a crucial role in customer experience management. By allowing machines to comprehend human language, NLU enables chatbots and virtual assistants to interact with customers more naturally, providing a seamless and satisfying experience.

NLU focuses on the “semantics” of the language, it can extract the real meaning from any given piece of text. Although computers could process multiple queries at once were versatile, multitaskers and what not but they lacked something. And  yes that something was “understanding of the human emotions”, it won’t be an exaggeration to say what appeared like an alien concept in the past has become a “reality of the present”. Understanding human language is a different thing but absorbing the real intent of the language is an altogether different scenario.

NLP tasks include text classification, sentiment analysis, part-of-speech tagging, and more. You may, for instance, use NLP to classify an email as spam, predict whether a lead is likely to convert from a text-form entry or detect the sentiment of a customer comment. Pushing the boundaries of possibility, natural language understanding (NLU) is a revolutionary field of machine learning that is transforming the way we communicate and interact with computers. Natural language understanding is a subset of machine learning that helps machines learn how to understand and interpret the language being used around them.

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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. The technology also utilizes semantic role labeling (SRL) to identify the roles and relationships of words or phrases in a sentence with respect to a specific predicate. In summary, NLU is critical to the success of AI-driven applications, as it enables machines to understand and interact with humans in a more natural and intuitive way.

It will derive meaning of every individual word and will later combine the meanings of these words. It will process the queries based on the combined meaning and show results based on the meaning of words. Whereas in NLP, it totally depends on how the machine is able to process the targeted spoken or written data and then take proper decisions and actions on how to deal with them. In NLU, the texts and speech don’t need to be the same, as NLU can easily understand and confirm the meaning and motive behind each data point and correct them if there is an error.

NLP vs NLU vs. NLG summary

The tech aims at bridging the gap between human interaction and computer understanding. 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. As humans, we can identify such underlying similarities almost effortlessly and respond accordingly.

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• 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.
• Customer reviews are analyzed via Sentiment Analysis and post analysis the data is delivered to the sales and marketing team of respective companies.
• It can analyze text to extract concepts, entities, keywords, categories, semantic roles and syntax.
• This text can also be converted into a speech format through text-to-speech services.

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. All this has sparked a lot of interest both from commercial adoption and academics, making NLP one of the most active research topics in AI today. The last place that may come to mind that utilizes NLU is in customer service AI assistants. For example, entity analysis can identify specific entities mentioned by customers, such as product names or locations, to gain insights into what aspects of the company are most discussed. Sentiment analysis can help determine the overall attitude of customers towards the company, while content analysis can reveal common themes and topics mentioned in customer feedback.

NLP undertakes various tasks such as parsing, speech recognition, part-of-speech tagging, and information extraction. 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.

NLG systems enable computers to automatically generate natural language text, mimicking the way humans naturally communicate — a departure from traditional computer-generated text. 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. When given a natural language input, NLU splits that input into individual words — called tokens — which include punctuation and other symbols. The tokens are run through a dictionary that can identify a word and its part of speech.

While natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) are all related topics, they are distinct ones. Given how they intersect, they are commonly confused within conversation, but in this post, we’ll define each term individually and summarize their differences to clarify any ambiguities. Now, businesses can easily integrate AI into their operations with Akkio’s no-code AI for NLU. With Akkio, you can effortlessly build models capable of understanding English and any other language, by learning the ontology of the language and its syntax. Even speech recognition models can be built by simply converting audio files into text and training the AI.

• The system has to be trained on an extensive set of examples to recognize and categorize different types of intents and entities.
• They also offer personalized recommendations based on user behavior and preferences, making them an essential part of the modern home and workplace.
• NLG also encompasses text summarization capabilities that generate summaries from in-put documents while maintaining the integrity of the information.
• 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.
• It can modify the work cases in multiple industries, it can perform many operations in the shortest possible time span.

Natural Language Understanding is also making things like Machine Translation possible. Machine Translation, also known as automated translation, is the process where a computer software performs language translation and translates text from one language to another without human involvement. Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer. This is done by identifying the main topic of a document and then using NLP to determine the most appropriate way to write the document in the user’s native language. Try out no-code text analysis tools like MonkeyLearn to  automatically tag your customer service tickets.

Examples of natural language understanding

By unlocking the insights in unstructured text and driving intelligent actions through natural language understanding, NLU can help businesses deliver better customer experiences and drive efficiency gains. In both intent and entity recognition, a key aspect is the vocabulary used in processing languages. The system has to be trained on an extensive set of examples to recognize and categorize different types of intents and entities.

NLU relies on NLP’s syntactic analysis to detect and extract the structure and context of the language, which is then used to derive meaning and understand intent. Processing techniques serve as the groundwork upon which understanding techniques are developed and applied. While both technologies are strongly interconnected, NLP rather focuses on processing and manipulating language and NLU aims at understanding and deriving the meaning using advanced techniques and detailed semantic breakdown. The distinction between these two areas is important for designing efficient automated solutions and achieving more accurate and intelligent systems. By way of contrast, NLU targets deep semantic understanding and multi-faceted analysis to comprehend the meaning, aim, and textual environment. NLU techniques enable systems to grasp the nuances, references, and connections within the text or speech resolve ambiguities and incorporate external knowledge for a comprehensive understanding.

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 converts the “written text” into structured data; parsing, speech recognition and part of speech tagging are a part of NLP. NLP breaks down the language into small and understable chunks that are possible for machines to understand. Akkio is used to build NLU models for computational linguistics tasks like machine translation, question answering, and social media analysis.

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. There are thousands of ways to request something in a human language that still defies conventional natural language processing. “To have a meaningful conversation with machines is only possible when we match every word to the correct meaning based on the meanings of the other words in the sentence – just like a 3-year-old does without guesswork.” NLP and NLU are similar but differ in the complexity of the tasks they can perform.

At this point, there comes the requirement of something called ‘natural language’ in the world of artificial intelligence. Language is how we all communicate and interact, but machines have long lacked the ability to understand human language. Akkio is an easy-to-use machine learning platform that provides a suite of tools to develop and deploy NLU systems, with a focus on accuracy and performance. NLU is the broadest of the three, as it generally relates to understanding and reasoning about language. NLP is more focused on analyzing and manipulating natural language inputs, and NLG is focused on generating natural language, sometimes from scratch.

According to Zendesk, tech companies receive more than 2,600 customer support inquiries per month. Using NLU technology, you can sort unstructured data (email, social media, live chat, etc.) by topic, sentiment, and urgency tickets can then be routed directly to the relevant agent and prioritized.

NLP focuses on processing and analyzing text data, such as language translation or speech recognition. NLU goes a step further by understanding the context and meaning behind the text data, allowing for more advanced applications such as chatbots or virtual assistants. With the help of natural language understanding (NLU) and machine learning, computers can automatically analyze data in seconds, saving businesses countless hours and resources when analyzing troves of customer feedback. Statistical models use machine learning algorithms such as deep learning to learn the structure of natural language from data.

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