IBM Watson Natural Language Understanding

Chatbots using NLG algorithms have been known to provide personalized customer engagement by responding with dynamic content that takes context into account. In the marketing domain, NLG is used for content creation purposes such as product descriptions, blog articles, or even social media posts. In finance, NLP is applied to analyze market trends, identify fraud cases or automate customer support responses. With voicebots, most voice applications use ASR (automatic speech recognition) first. Chatbots offer 24-7 support and are excellent problem-solvers, often providing instant solutions to customer inquiries. These low-friction channels allow customers to quickly interact with your organization with little hassle.

Get underneath your data using text analytics to extract categories, classification, entities, keywords, sentiment, emotion, relations and syntax. NLU provides support by understanding customer requests and quickly routing them to the appropriate team member. Because NLU grasps the interpretation and implications of various customer requests, it’s a precious tool for departments such as customer service or IT. It has the potential to not only shorten support cycles but make them more accurate by being able to recommend solutions or identify pressing priorities for department teams. Although natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) are similar topics, they are each distinct. You can type text or upload whole documents and receive translations in dozens of languages using machine translation tools.

Examples of Natural Language Processing in Action

NLU-powered chatbots can provide instant, 24/7 customer support at every stage of the customer journey. This competency drastically improves customer satisfaction by establishing a quick communication channel to solve common problems. 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. NLU is essential in developing question-answering systems that understand and respond to user questions. These systems utilize NLU techniques to comprehend questions’ meaning, context, and intent, enabling accurate and relevant answers.

It’s likely only a matter of time before you’re asked to design or build a chatbot or voice assistant. Now that you know the basics, you should have what it takes to be able to talk about NLU with a degree of understanding, and maybe even enough to start using NLU systems to create conversational assistants right away. NLU systems work by analysing input text, and using that to determine the meaning behind the user’s request. It does that by matching what’s said to training data that corresponds to an ‘intent’. Most of the time, NLU is found in chatbots, voicebots and voice assistants, but it can theoretically be used in any application that aims to understand the meaning of typed text.

Syntactic Analysis

There are various ways that people can express themselves, and sometimes this can vary from person to person. Especially for personal assistants to be successful, an important point is the correct understanding of the user. NLU transforms the complex structure of the language into a machine-readable structure.

It formulates answers to questions in natural language and is widely applied in end-use applications by various enterprises. NLU formulates meaningful responses based on the learning curve for the machine. NLU Chatbots can make the support process easier, faster and more convenient for users, support staff and enterprises. Automated responses driven by predetermined patterns of user correspondence are fed into the programing of chatbots to generate default responses for frequently asked queries and questions. It helps reduce response time, optimize human resource deployment and control costs for enterprises. This could include analyzing emotions to understand what customers are happy or unhappy about.

Performing a manual review of complex documents can be a very cumbersome, tiring, and time-consuming ordeal. Moreover, mundane and repetitive tasks are often at risk of human error, which can result in dire repercussions if the target documents are of a sensitive nature. Let’s take an example of how you could lower call center costs and improve customer satisfaction using NLU-based technology. SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items.

Finding the right balance between precision and simplification is an ongoing debate among NLG practitioners. For example, suppose you are using an e-commerce platform that incorporates NLG technology to generate product descriptions automatically. In that case, the system might use different templates for different categories of products such as electronics versus skincare items.

Things to pay attention to while choosing NLU solutions

Because NLG systems rely on complex algorithms and machine learning models, it can be difficult to understand how they arrive at certain conclusions or generate specific narratives. This can be a problem for businesses that need to ensure the output of an NLG system is fair, unbiased, and accurate. For example, suppose you are using a voice assistant device that employs NLP technology to understand your spoken commands and perform various tasks such as setting alarms how does natural language understanding (nlu) work? or playing music. In that case, the system might use pre-trained models of speech recognition that can recognize your accent or vocal tone while ignoring background noise or other irrelevant input. Once the system has understood your intent, it can then generate an appropriate output response using NLG techniques. Natural language understanding can positively impact customer experience by making it easier for customers to interact with computer applications.

A quick overview of the integration of IBM Watson NLU and accelerators on Intel Xeon-based infrastructure with links to various resources. Accelerate your business growth  as an Independent Software Vendor (ISV) by innovating with IBM. Partner with us to deliver enhanced commercial solutions embedded with AI to better address clients’ needs.

NLU tools allow data scientists to process large volumes of natural language text into coherent groups without reading them all. Data-to-text conversion involves mapping the input data to natural language sentences. This can be done using templates or rules-based systems, but many modern NLG systems use machine learning algorithms to generate more natural-sounding text. As with any technology, natural language generation has seen some significant advancements over the years.

Additionally, some AI struggles with filtering through inconsequential words to find relevant information. When people talk to each other, they can easily understand and gloss over mispronunciations, stuttering, or colloquialisms. Even though using filler phrases like “um” is natural for human beings, computers have struggled to decipher their meaning. For example, insurance organizations can use it to read, understand, and extract data from loss control reports, policies, renewals, and SLIPs.

Why should every business start using natural language understanding?

This approach allows NLP and NLG systems to be highly adaptable to different contexts and languages. IBM Watson NLP Library for Embed, powered by Intel processors and optimized with Intel software tools, uses deep learning techniques to extract meaning and meta data from unstructured data. Additionally, NLU systems can use machine learning algorithms to learn from past experience and improve their understanding of natural language. Understanding the opinions, needs, and desires of customers is one of the main priorities of organizations and brands.

• As with any technology, natural language generation has seen some significant advancements over the years.
• While the tool was able to generate descriptions for thousands of products quickly and efficiently, the company found that the descriptions weren’t actually very helpful to customers.
• This text is then broken down into smaller pieces, often at the word or phrase level, in a process known as tokenization.

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. NLU enables a computer to understand human languages, even the sentences that hint towards sarcasm can be understood by Natural Language Understanding (NLU). In addition to machine learning, deep learning and ASU, we made sure to make the NLP (Natural Language Processing) as robust as possible.

When a customer service ticket is generated, chatbots and other machines can interpret the basic nature of the customer’s need and rout them to the correct department. Companies receive thousands of requests for support every day, so NLU algorithms are useful in prioritizing tickets and enabling support agents to handle them in more efficient ways. Semantic analysis applies computer algorithms to text, attempting to understand the meaning of words in their natural context, instead of relying on rules-based approaches. The grammatical correctness/incorrectness of a phrase doesn’t necessarily correlate with the validity of a phrase.

Simplilearn’s AI ML Certification is designed after our intensive Bootcamp learning model, so you’ll be ready to apply these skills as soon as you finish the course. You’ll learn how to create state-of-the-art algorithms that can predict future data trends, improve business decisions, or even help save lives. Request a demo and our team will help you build a chatbot that is not only powered by our cutting-edge NLP engine but also understands 100+ languages and can be deployed to more than 35 channels with a single click. Chatbots, when equipped with Artificial Intelligence (AI) and Natural Language Understanding(NLU), can generate more human-like conversations with the users. Digital assistants equipped with the NLU abilities can deduce what the user ‘actually’ means, regardless of how it is expressed. This is important for applications that need to deal with a vast vocabulary and complex syntaxes, such as chatbots and writing assistants.