There is Natural Language Understanding at work as well, helping the voice assistant to judge the intention of the question. 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. Request a demo and begin your natural language understanding journey in AI. Natural language processing and understanding have found use cases across the channels of customer service.
What is the difference between NLU and NLP?
Natural Language Understanding (NLU) is a subset of Natural Language Processing (NLP). NLP is a field that incorporates both linguistics and computer science to improve the communication between humans and AI. Meanwhile, NLU is the discipline within NLP that specifically deals with AI’s capacity to understand human speech.
An entity in NLU is any word or phrase that can be used to add additional context to a message. NLU entities can be people, objects, locations, or even abstract ideas. Examples of these include quantities, dates, times, currencies, and percentages. Basic natural language text concepts understood through NLU include locations, and dates.
NLP vs NLU vs. NLG summary
Based on some data or query, an NLG system would fill in the blank, like a game of Mad Libs. But over time, natural language generation systems have evolved with the application of hidden Markov chains, recurrent neural networks, and transformers, enabling more dynamic text generation in real time. The customer experience can be substantially improved thanks to fast and powerful AI calculations, which create a seamless conversational flow between brands and consumers. While the awareness of entities in a body of text may be remarkable, the true wonder of NLU is its capacity for intent classification. Through this competency, an NLU-powered machine is able to recognize what people are trying to achieve. This way, NLU can be used to improve customer service, sales, and many other business undertakings.
While NLU parses text for information, NLG uses the data gleaned from NLU to generate authentic speech. Interestingly, the concepts of natural language processing and natural language understanding are very often used interchangeably. However, there are differences between them even though there are overlaps too.
Natural language understanding applications
However, if a developer wants to build an intelligent contextual assistant capable of having sophisticated natural-sounding conversations with users, they would need NLU. NLU is the component that allows the contextual assistant to understand the intent of each utterance by a user. Without it, the assistant won’t be able to understand what a user means throughout a conversation. And if the assistant doesn’t understand what the user means, it won’t respond appropriately or at all in some cases. Natural Language Understanding is an area of artificial intelligence to process input data provided by the user in natural language say text data or speech data.
As an open source NLP tool, this work is highly visible and vetted, tested, and improved by the Rasa Community. Open source NLP for any spoken language, any domain Rasa Open Source provides natural language processing that’s trained entirely on your data. This enables you to build models for any language and any domain, and your model can learn to recognize terms that are specific to your industry, like insurance, financial services, or healthcare. Learn how to extract and classify text from unstructured data with MonkeyLearn’s no-code, low-code text analysis tools.
Turn human language into structured data
NLP can analyze text and speech, performing a wide range of tasks that focus primarily on language structure. However, it will not tell you what was meant or intended by specific language. NLU allows computer applications to infer intent from language even when the written or spoken language is flawed. Natural language processing is a subset of AI, and it involves programming computers to process massive volumes of language data. It involves numerous tasks that break down natural language into smaller elements in order to understand the relationships between those elements and how they work together. Common tasks include parsing, speech recognition, part-of-speech tagging, and information extraction.
Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability. For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to Difference Between NLU And NLP mimic the English speaking computer in Star Trek. Vulcan later became the dBase system whose easy-to-use syntax effectively launched the personal computer database industry. Systems with an easy to use or English like syntax are, however, quite distinct from systems that use a rich lexicon and include an internal representation of the semantics of natural language sentences.
Solutions for Retail & CPG
ELIZA worked by simple parsing and substitution of key words into canned phrases and Weizenbaum sidestepped the problem of giving the program a database of real-world knowledge or a rich lexicon. Yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask.com. It will use NLP and NLU to analyze your content at the individual or holistic level.