Main Principles
For a machine to be autonomous, the main principle is the ability to communicate in the natural language known to humans. In the vast world of artificial intelligence, one area is concerned with making machines interact using these languages: Natural Language Processing (NLP). NLP is a general term that covers everything related to building machines capable of processing natural language, receiving and understanding input, or generating a response. Another used term is Natural Language Understanding (NLU). NLP and NLU focus on different areas.Automate 84% of user questions
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Main Reasons to Use
The need for NLU and NLP has grown with advances in technology and research. Computers analyze and perform tasks on all data kinds. But when we talk about human language, it changes the whole script because it’s messy and ambiguous. It’s hard to handle human language. The system must recognize:- content;
- mood;
- purpose of human language.
- What is the weather like today?
- Will it be raining today?
- Do I need an umbrella today?
Natural Language Processing
NLP is a subset of AI and allows machines to interact using natural languages. The NLP domain also ensures that machines can:- Process large amounts of data in natural language;
- Extract ideas and information;
- Standardize text before translating.
- Parsing;
- Removing stop words;
- Part-of-speech (POS) marking;
- Tokenization, etc.
Natural Language Understanding
NLU helps the machine understand data. It interprets the data to process its meaning. Various rules, techniques, and models are used. There are three levels of language understanding.- Syntax: understands sentences and phrases and checks the text’s grammar and syntax;
- Semantic: checks the meaning of the text;
- Pragmatic: understands the context to know what the text is trying to achieve.
- semantic formulation;
- analysis;
- dialog agents, etc.
- Interpret natural language;
- get value;
- define context;
- draw ideas.
- Banks will be closed for Thanksgiving.
- The river will overflow its banks during floods.
NLU vs NLP: Key Difference
NLP looks at what we said, and NLU looks at what we meant. People can make mistakes when they write or speak. They use the wrong words, write incomplete sentences, misspell or mispronounce words. NLP analyzes text and speech, focusing on language structure. NLU allows computer applications to draw inferences from a language even if the written or spoken language is not perfect.Comparing NLP vs NLU
If developers use NLP and several machine learning techniques, they want to create a simple chatbot that gives a series of pre-programmed responses. However, if developers want to create an intelligent contextual assistant capable of having complex, natural-sounding conversations with users. They will need a NLU that allows the context helper to understand the intent of each user’s words. Without it, the assistant will not be able to understand what the user means during the conversation. And if the helper doesn’t understand what the user represents, it won’t respond appropriately, or it won’t react at all in some cases. Whether it’s simple chatbots or sophisticated Artificial Intelligence assistants, NLP is integral to building a conversational app. And the difference between NLP and NLU is critical to keep in mind when creating conversational applications because it affects how well the application interprets what users say and mean.Automate 84% of user questions
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Conclusion
NLP and NLU are essential terms for designing a machine that can easily understand human language, whether or not it contains some common flaws. There is a difference between NLP vs NLU terms. They are significant for developers to know if they want to create:- a machine interacting with humans;
- a human-like environment.