NLP vs NLU: What’s the Difference and Why Does it Matter? The Rasa Blog

‍In order to help someone, you have to first understand what they need help with. Machine learning can be useful in gaining a basic grasp on underlying customer intent, but it alone isn’t sufficient to gain a full understanding of what a user is requesting. Technology continues to advance and contribute to various domains, enhancing human-computer interaction and enabling machines to comprehend and process language inputs more effectively.

Rather than using human resource to provide a tailored experience, NLU software can capture, process and react to the large quantities of unstructured data that customers provide at scale. Two key concepts in natural language processing are intent recognition and entity recognition. Natural Language Generation is the production of human language content through software. Text in a defined source language is fed into such a model, and the output is text in a specified target language. These models are used to increase communication between users on social media networks like Facebook and Skype.

The difference between NLU and NLP

Named entities would be divided into categories, such as people’s names, business names and geographical locations. Numeric entities would be divided into number-based categories, such as quantities, dates, times, percentages and currencies. 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. Natural Language Understanding is a subset area of research and development that relies on foundational elements from Natural Language Processing (NLP) systems, which map out linguistic elements and structures.

Natural language processing works by taking unstructured data and converting it into a structured data format. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling. NLP is a branch of artificial intelligence (AI) that bridges human and machine language to enable more natural human-to-computer communication. When information goes into a typical NLP system, it goes through various phases, including lexical analysis, discourse integration, pragmatic analysis, parsing, and semantic analysis.

The ultimate of NLP is to read, decipher, understand, and make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples of such tasks are online chatbots, text summarizers, auto-generated keyword tabs, as well as tools analyzing the sentiment of a given text. 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.

Understanding How NLU and NLP Work Together to Make Sense of Language

Through the combination of these two components of NLP, it provides a comprehensive solution for language processing. It enables machines to understand, generate, and interact with human language, opening up possibilities for applications such as chatbots, virtual assistants, automated report generation, and more. Natural Language Generation (NLG) is an essential component of Natural Language Processing (NLP) that complements the capabilities of natural language understanding. While NLU focuses on interpreting human language, NLG takes structured and unstructured data and generates human-like language in response. NLU, on the other hand, is more concerned with the higher-level understanding. It aims to highlight appropriate information, guess context, and take actionable insights from the given text or speech data.

It’s a subset of artificial intelligence and has many applications, such as speech recognition, translation and sentiment analysis. Machine learning is at the core of natural language understanding (NLU) systems. It allows computers to “learn” from large data sets and improve their performance over time. Machine learning algorithms use statistical methods to process data, recognize patterns, and make predictions.

Using data modelling to learn what we really mean

Together, they are enabling a range of applications that are revolutionizing the way people interact with machines. However, true understanding of natural language is challenging due to the complexity and nuance of human communication. Machine learning approaches, such as deep learning and statistical models, can help overcome these obstacles by analyzing large datasets and finding patterns that aid in interpretation and understanding.

Figure 4 depicts our sample of 5 use cases in which businesses should favor NLP over NLU or vice versa. 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 Understanding, a field that sits at the nexus of linguistics, computer science, and artificial intelligence, has opened doors to innovations we once only dreamt of. From voice assistants to sentiment analysis, the applications are as vast as they are transformative. However, as with all powerful tools, the challenges — be it biases, privacy, or transparency — demand our attention.

How do NLU and NLP interact?

There are various semantic theories used to interpret language, like stochastic semantic analysis or naive semantics. Natural language understanding (NLU) is a technical concept within the larger topic of natural language processing. NLU is the process responsible for translating natural, human words into a format that a computer can interpret.

Voice recognition software can analyze spoken words and convert them into text or other data that the computer can process. Agents can also help customers with more complex issues by using NLU technology combined with natural language generation tools to create personalized responses based on specific information about each customer’s situation. Text generation, often known as natural language generation (NLG), generates text that resembles human-written text. Such models can be fine-tuned to generate text in a variety of genres and formats, such as tweets, blogs, and even computer code. Markov processes, LSTMs, BERT, GPT-2, LaMDA, and other techniques were used to generate text.

NLU-enabled technology will be needed to get the most out of this information, and save you time, money and energy to respond in a way that consumers will appreciate. Using our example, an unsophisticated software tool could respond by showing data for all types of transport, and display timetable information rather than links for purchasing tickets. Without being able to infer intent accurately, the user won’t get the response they’re looking for. Rather than relying on computer language syntax, Natural Language Understanding enables computers to comprehend and respond accurately to the sentiments expressed in natural language text. 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.

The right market intelligence software can give you a massive competitive edge, helping you gather publicly available information quickly on other companies and individuals, all pulled from multiple sources. This can be used to automatically create records or combine with your existing CRM data. With NLU integration, this software can better understand and decipher the information it pulls from the sources. Natural language understanding (NLU) is where you take an input text string and analyse what it means. For instance, when a person reads someone’s question on Twitter and responds with an answer accordingly (small scale) or when Google parses thousands to millions of documents to understand what they are about (large scale).

For computers to get closer to having human-like intelligence and capabilities, they need to be able to understand the way we humans speak. On top of these deep learning models, we have developed a proprietary algorithm called ASU (Automatic Semantic Understanding). ASU works alongside the deep learning models and tries to find even more complicated connections between the sentences in a virtual agent’s interactions with customers. A typical machine learning model for text classification, by contrast, uses only term frequency (i.e. the number of times a particular term appears in a data corpus) to determine the intent of a query. Oftentimes, these are also only simple and ineffective keyword-based algorithms. In practical applications such as customer support, recommendation systems, or retail technology services, it’s crucial to seamlessly integrate these technologies for more accurate and context-aware responses.

• NLU helps computers comprehend the meaning of words, phrases, and the context in which they are used.
• You’re also using it to analyze blog posts to match content to known search queries.
• 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.
• 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.
• Ultimately, we can say that natural language understanding works by employing algorithms and machine learning models to analyze, interpret, and understand human language through entity and intent recognition.
• Chatbots are necessary for customers who want to avoid long wait times on the phone.

The question “what’s the weather like outside?” can be asked in hundreds of ways. With NLU, computer applications can recognize the many variations in which humans say the same things. Natural language processing is generally more suitable for tasks involving data extraction, text summarization, and machine translation, among others.

Let’s take an example of how you could lower call center costs and improve customer satisfaction using NLU-based technology. Autocomplete guesses the next word, and autocomplete systems of increasing sophistication are utilized in chat apps such as WhatsApp. GPT-2 is a well-known autocomplete model that has been used to produce essays, song lyrics, and much more.

Natural language understanding software can help you gain a competitive advantage by providing insights into your data that you never had access to before. Parsing is only one part of NLU; other tasks include sentiment analysis, entity recognition, and semantic role labeling. Both ‘you’ and ‘I’ in the above sentences are known as stopwords and will be ignored by traditional algorithms. Deep learning models (without the removal of stopwords) understand how these words are connected to each other and can, therefore, infer that the sentences are different.

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