What is NLU?
One easy way to understand NLU is by looking at available consumer services and business products that model natural language understanding. For example, Apple’s Siri or Amazon’s Alexa performs natural language understanding work in the context of hearing and deciphering user inputs.
A similar natural language understanding engine is built into Amazon “Lex,” an enterprise service for building machine learning platforms. By understanding how natural language understanding is applied to these applications, it is easy to see how natural language understanding involves the comprehension of language input.
From the computer’s point of view, any natural language is a free from text. That means there are no set keywords at set positions when providing an input.
Beyond the unstructured nature, there can also be multiple ways to express something using a natural language. For example, consider these three sentences:
- How is the weather today?
- Is it going to rain today?
- Do I need to take my umbrella today?
All these sentences have the same underlying question, which is to enquire about today’s weather forecast.
As humans, we can identify such underlying similarities almost effortlessly and respond accordingly. But this is a problem for machines—any algorithm will need the input to be in a set format, and these three sentences vary in their structure and format. And if we decide to code rules for each and every combination of words in any natural language to help a machine understand, then things will get very complicated very quickly.
This is where NLP enters the picture.
NLP is a subset of AI tasked with enabling machines to interact using natural languages. The domain of NLP also ensures that machines can:
- Process large amounts of natural language data
- Derive insights and information
How does It work?
NLU analyzes information to decide its which means via way of means of the use of algorithms to lessen human speech right into a established form, a information version including semantics and practical definitions. Two essential standards of NLU are motive and entity reputation.
Intent reputation is the procedure of figuring out the user’s sentiment in enter textual content and figuring out their objective. It is the primary and maximum vital a part of NLU as it establishes the which means of the textual content.
Entity reputation is a selected sort of NLU that specializes in figuring out the entities in a message, then extracting the maximum important facts. Approximately the ones entities. There are sorts of entities: named entities and numeric entities. Named entities are grouped in categories that include people, organizations and locations. Numerical entities are numbers, currencies and percentages. Numerical entities are numbers, currencies and percentages.
For example, “‘I want to book a ticket from DELHI to COCHIN”, here both Delhi and Cochin are entities(location)
Differences Between NLP,NLU and NLG
NLU is a subset of natural language processing (NLP). NLP attempts to analyze and understand the text of a particular document.
Let’s take an example sentence:
- Please crack the windows, the car is getting hot.
NLP mainly , focuses on processing text in the literal sense. Conversely, NLU focuses on extracting context and intent, meaning.
NLP will take the request to crack the windows in the literal sense, but it will be NLU which will help draw the inference, that the user may be intending to roll down the windows.
Natural language processing (NLP)
Natural language processing (NLP) is a field of artificial intelligence in which computers analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
Apart from common word processor operations that treat text like a mere sequence of symbols, NLP considers the hierarchical structure of language: several words make a phrase, several phrases make a sentence and, ultimately, sentences convey ideas,” John Rehling, an NLP expert at Meltwater Group, says in How Natural Language Processing Helps Uncover Social Media Sentiment. “By looking language for its meaning, NLP systems have long filled useful roles, such as correcting grammar, converting speech to text and automatically translating between languages.”
Natural language generation (NLG)
NLP is used to analyze text, allowing machines to undestand how human speech work
Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set. NLG is related to human-to-machine and machine-to-human interaction, including computational linguistics, natural language processing (NLP) and natural language understanding (NLU).
Research about NLG often focuses on building computer programs that provide data points with context. Sophisticated NLG software can mine large quantities of numerical data. NLG software is really useful for producing news and other time-sensitive stories on the internet.
Data collection is the process of collecting and recording information about an object, person, or event. Assume your e-commerce company uses NLU, in which case clients must provide shipping and billing information verbally. The software understands the customer’s meaning and automatically fills in the information.
Customer care and service with an intelligent personal assistant
NLU is the technology behind chatbots, computer programs that communicate with humans in natural language via text or voice. Chatbots can follow a script and answer, only the questions in that script. These smart personal assistants, are a convenient addition to customer service. As an example, chatbots are usually provide answers to frequently asked questions. To achieve this, NLU technology requires different layers of processes. Feature extraction and classification, entity linking, knowledge management.
Many voice-enabled devices, including Amazon Alexa and Google Home, allow users to speak naturally. With NLU, conversational interfaces can understand and respond to human voice, by segmenting words and phrases, recognizing grammar, and using semantic knowledge to infer intent.