07 يوليو NLP vs NLU: Whats The Difference? BMC Software Blogs
what is natural language understanding
Organizations need artificial intelligence solutions that can process and understand large (or small) volumes of language data quickly and accurately. These solutions should be attuned to different contexts and be able to scale along with your organization. Machines may be able to read information, but comprehending it is another story. For example, “moving” can mean physically moving objects or something emotionally resonant.
In short, NLU brings a lot of varied business value; however, it is important to remember that NLU is only a subset of NLP capabilities, which are required to provide “smart” answers to “smart” questions. NLU only tells half of the story, or rather, it only asks the question, a smart search engine delivers the answer. NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. With AI-driven thematic analysis software, you can generate actionable insights effortlessly.
There’s a growing need for understanding at scale
The more the NLU system interacts with your customers, the more tailored its responses become, thus, offering a personalised and unique experience to each customer. Natural language understanding (NLU) refers to a computer’s ability to understand or interpret human language. Once computers learn AI-based natural language understanding, they can serve a variety of purposes, such as voice assistants, chatbots, and automated translation, to name a few. At its most basic, sentiment analysis can identify the tone behind natural language inputs such as social media posts. Taking it further, the software can organize unstructured data into comprehensible customer feedback reports that delineate the general opinions of customers. This data allows marketing teams to be more strategic when it comes to executing campaigns.
A number of advanced NLU techniques use the structured information provided by NLP to understand a given user’s intent. In the lingo of chess, NLP is processing both the rules of the game and the current state of the board. An effective NLP system takes in language and maps it — applying a rigid, uniform system to reduce its complexity to something a computer can interpret. Matching word patterns, understanding synonyms, tracking grammar — these techniques all help reduce linguistic complexity to something a computer can process.
Learn how to add smartness to your bot
Gain business intelligence and industry insights by quickly deciphering massive volumes of unstructured data. Your NLU software takes a statistical sample of recorded calls and performs speech recognition after transcribing the calls to text via MT (machine translation). The NLU-based text analysis links specific speech patterns to both negative emotions and high effort levels.
Essentially, NLP processes what was said or entered, while NLU endeavors to understand what was meant. The intent of what people write or say can be distorted through misspelling, fractured sentences, and mispronunciation. NLU pushes through such errors to determine the user’s intent, even if their written or spoken language is flawed. NLP involves processing natural spoken or textual language data by breaking it down into smaller elements that can be analyzed. Common NLP tasks include tokenization, part-of-speech tagging, lemmatization, and stemming. There are 4 key areas where the power of NLU can help companies improve their customer experience.
Importance of Natural Language Understanding
Get underneath your data using text analytics to extract categories, classification, entities, keywords, sentiment, emotion, relations and syntax. Natural language understanding works by deciphering the overall meaning (or intent) of a text. Rather than training an AI model to recognize keywords, NLU processes language in the same way that people understand speech — taking grammatical rules, sentence structure, vocabulary, and semantics into account. It’s frustrating to feel misunderstood, whether you’re communicating with a person or a bot. This is where natural language understanding — a branch of artificial intelligence — comes in. For example, the chatbot could say, “I’m sorry to hear you’re struggling with our service.
- Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between machines and human (natural) languages.
- NLU enables computers to understand the sentiments expressed in a natural language used by humans, such as English, French or Mandarin, without the formalized syntax of computer languages.
- In addition to making chatbots more conversational, AI and NLU are being used to help support reps do their jobs better.
- In Figure 2, we see a more sophisticated manifestation of NLP, which gives language the structure needed to process different phrasings of what is functionally the same request.
These smart personal assistants, are a convenient addition to customer service. As an example, chatbots are usually provide answers to frequently asked questions. Feature extraction and classification, entity linking, knowledge management. NLU is widely used in virtual assistants, chatbots, and customer support systems.
As artificial intelligence (AI) continues to evolve, businesses that adopt NLU will have a competitive advantage. So if you still need to start using NLU, now is the time to explore its potential for your business. In 1971, Terry Winograd finished writing SHRDLU for his PhD thesis at MIT. SHRDLU could understand simple English sentences in a restricted world of children’s blocks to direct a robotic arm to move items. Bharat Saxena has over 15 years of experience in software product development, and has worked in various stages, from coding to managing a product.
There can be phrases that are grammatically correct yet meaningless, and phrases that are grammatically incorrect yet have meaning. In order to distinguish the most meaningful aspects of words, NLU applies a variety of techniques intended to pick up on the meaning of a group of words with less reliance on grammatical structure and rules. NLU’s customer support feature has become so valuable for digital platforms that they can manage to offer essential solutions to customers and quickly transform the critical message to technical teams. AI-based chatbots are becoming irreplaceable as they offer virtual reality-based tours of all major products to customers without making them pay a visit to physical stores. Hence the breadth and depth of “understanding” aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The “breadth” of a system is measured by the sizes of its vocabulary and grammar.
However, solutions like the Expert.ai Platform have language disambiguation capabilities to extract meaningful insight from unstructured language data. Natural Language Understanding (NLU) is a branch of Artificial Intelligence that enables computers to interpret and understand human language. By using natural language processing (NLP) techniques, NLU technology can interpret what a person says, so that computers can better understand and respond to requests, commands, and questions. This is extremely useful for resolving tasks like topic modelling, machine translation, content analysis, and question-answering at volumes which simply would not be possible to resolve using human intervention alone. NLU is one of the most important areas of NLP as it makes it possible for machines to understand us.
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