Natural languages differ from formal and artificial ones. Natural language is used and repeated. Planning and strategy are not expected. German, English, Spanish, and other languages have evolved during their use.
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.
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Another used term is Natural Language Understanding (NLU). NLP and NLU focus on different areas.
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:
purpose of human language.
But it is essential to understand human speech to know the client’s intentions for a successful business. Here NLU and NLP play a vital role in processing human language.
From a computer’s point of view, any natural language is a free-form text with no given keywords in the given positions when entering the data.
Consider these three sentences:
What is the weather like today?
Will it be raining today?
Do I need an umbrella today?
These sentences ask the same thing – today’s weather forecast.
We can effortlessly identify such underlying similarities and respond accordingly as humans. But this is a problem for machines — any algorithm will require input in a given format, and these three sentences differ in their structure and layout. If we decide to encode the rules for every word combination in any natural language to help the machine understand, things get complicated very quickly. And NLP comes into play.
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.
In machine learning (ML), the sequence of steps taken is called data preprocessing. The idea is to break up natural language text into smaller, manageable chunks. ML algorithms parse them to find relationships, dependencies, and context between different fragments.
Some examples of preprocessing steps:
Removing stop words;
Part-of-speech (POS) marking;
Thus, the NLP goal is to process natural language text in free form to transform it into a standardized structure.
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.
The NLU must understand misspelled unstructured text in a structured and well-formed format. It is used for:
dialog agents, etc.
NLU converts text into a machine-readable format. Let’s look at an example for more clarity. If you asked: “How are you today?”. What if the system answers: “Today is October 1, 2020, Thursday.” Does it give you the correct answer? No, because users want to know about the weather. Therefore, we use NLU to find out the right meaning of some words in the text.
Considered a sub-topic of NLP, the main task of understanding natural language is to create machines:
Interpret natural language;
NLU uses various ML algorithms to detect sentiment, perform Entity Name Recognition (NER), process semantics, etc. NLU algorithms often operate on text standardized by text preprocessing steps.
Returning to our weather query example, NLU lets the machine know that these three different queries have the same underlying weather query. The same words can have different meanings depending on how they are used. Let’s take another example:
Banks will be closed for Thanksgiving.
The river will overflow its banks during floods.
A task called word disambiguation, which is under the auspices of the NLU, ensures that a machine can understand two different meanings in which the word “bank” is used.
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.
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.
Using the proper technique in the right place is essential for success in systems designed for natural language operations.