Natural Language Understanding NLU: Revolutionizing AI’s Understanding of Human Language
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)
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. There are various entities extractor available such as CRFEntityExtractor, MitieEntityExtractor, EntitySynonymMapper, etc through which you can train your custom entities. There are two approaches to create conversational agent namely Rule-based and Self Learning/Machine Learning.
- There are two approaches to create conversational agent namely Rule-based and Self Learning/Machine Learning.
- Considering the complexity of language, creating a tool that bypasses significant limitations such as interpretations and context can be ambitious and demanding.
- At its core, NLU acts as the bridge that allows machines to grasp the intricacies of human communication.
- It can easily capture, process, and react to these unstructured, customer-generated data sets.
- You can see more reputable companies and media that referenced AIMultiple.
- It could also produce sales letters about specific products based on their attributes.
This is a practical way of combining different Intent collections into a bigger model. Uploading intents does not delete existing intents that are not included in the upload file. If you want to delete intents, you can use the Delete All Intents option or delete individual intents beforehand. Cognigy NLU comes with an intent confirmation mechanism that works by configuring Confirmation Sentences in each intent. Whenever an Intent score falls within a (configurable) range – let’s say 0,4 – 0,6, the Confirmation Sentence is triggered and shown to the user. Head over to Fast Data Science’s comprehensive guide on NLU to expand your understanding of this fascinating AI domain.
Natural Language Processing (NLP): 7 Key Techniques
Natural language understanding is a subfield of natural language processing. Over 60% say they would purchase more from companies they felt cared about them. Part of this caring is–in addition to providing great customer service and meeting expectations–personalizing the experience for each individual. With today’s mountains of unstructured data generated daily, it is essential to utilize NLU-enabled technology. The technology can help you effectively communicate with consumers and save the energy, time, and money that would be expensed otherwise. Natural language generation (NLG) is a process within natural language processing that deals with creating text from data.
Why neural networks aren’t fit for natural language understanding – TechTalks
Why neural networks aren’t fit for natural language understanding.
Posted: Mon, 12 Jul 2021 07:00:00 GMT [source]
Banking and finance organizations can use NLU to improve customer communication and propose actions like accessing wire transfers, deposits, or bill payments. Life science and pharmaceutical companies have used it for research purposes and to streamline their scientific information management. NLU can be a tremendous asset for organizations across multiple industries by deepening insight into unstructured language data so informed decisions can be made. Essentially, it’s how a machine understands user input and intent and “decides” how to respond appropriately. In the realm of customer service, NLU-powered chatbots are transforming the way companies engage with their clients.
Recommenders and Search Tools
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. 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. Essentially, before a computer can process language data, it must understand the data. 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.
When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing. As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills. 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.
Machine Learning Projects in Healthcare
You’re falling behind if you’re not using NLU tools in your business’s customer experience initiatives. Due to the fluidity, complexity, and subtleties of human language, it’s often difficult for two people to listen or read the same piece of text and walk away with entirely aligned interpretations. Before booking a hotel, customers want to learn more about the potential accommodations. People start asking questions about the pool, dinner service, towels, and other things as a result. Such tasks can be automated by an NLP-driven hospitality chatbot (see Figure 7). Congratulations, we have successfully built our intent classifier which can understand the purpose of the user’s utterance.
![nlu in 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)
In the second half of the course, you will pursue an original project in natural language understanding with a focus on following best practices in the field. Additional lectures and materials will cover important topics to help expand and improve your original system, including evaluations and metrics, semantic parsing, and grounded language understanding. NLU works by processing large datasets of human language using Machine Learning (ML) models. These models are trained on relevant training data that help them learn to recognize patterns in human language.
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. Natural language understanding can positively impact customer experience by making it easier for customers to interact with computer applications. For example, NLU can be used to create chatbots that can simulate human conversation. These chatbots can answer customer questions, provide customer support, or make recommendations. NLU systems use these three steps to analyze a text and extract its meaning. Additionally, NLU systems can use machine learning algorithms to learn from past experience and improve their understanding of natural language.
![nlu in 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)
One crucial aspect that empowers AI to comprehend human language is natural language understanding (NLU). NLU plays a pivotal role in converting natural language into a structured format, facilitating tasks such as sentiment analysis and entity recognition. In this comprehensive blog, the significance of NLU is explored along with its distinctions from natural language processing (NLP) and natural language generation (NLG).
You can type text or upload whole documents and receive translations in dozens of languages using machine translation tools. Google Translate even includes optical character recognition (OCR) software, which allows machines to extract text from images, read and translate it. For example, the chatbot could say, “I’m sorry to hear you’re struggling with our service. I would be happy to help you resolve the issue.” This creates a conversation that feels very human but doesn’t have the common limitations humans do. Typical computer-generated content will lack the aspects of human-generated content that make it engaging and exciting, like emotion, fluidity, and personality.
- With NLU, conversational interfaces can understand and respond to human language.
- Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment.
- Cognigy NLU comes with an intent confirmation mechanism that works by configuring Confirmation Sentences in each intent.
- The technology can help you effectively communicate with consumers and save the energy, time, and money that would be expensed otherwise.
- Due to the fluidity, complexity, and subtleties of human language, it’s often difficult for two people to listen or read the same piece of text and walk away with entirely aligned interpretations.
Finally, Copilot can help you create more accessible and inclusive solutions by supporting multiple languages and formats. Copilot is powered by Azure OpenAI Service, a cloud-based platform that offers access to massive large language models (LLMs), such as GPT-4. These LLMs are trained on billions of nlu in ai lines of text from multiple sources, allowing them to generate natural language text across a wide range of domains and tasks. NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis.