What is Natural Language Processing in Artificial Intelligence?

1. NLP Tutorial – Objective

In this NLP AI Tutorial, we will study what is NLP in Artificial Language. Moreover, we will discuss the components of Natural Language Processing and NLP applications. Along with this, we will learn the process, steps, importance and examples of NLP.

So, let’s start Natural Language Processing in AI Tutorial.

What is Natural Language Processing in Artificial Intelligence?

What is Natural Language Processing in Artificial Intelligence?

2. What is NLP (Natural Language Processing)?

We use the English language to communicate between an intelligent system and N.L.P. Processing of Natural Language plays an important role in various systems.
For Example:
A robot, it is used to perform as per your instructions. The input and output of an N.L.P system can be −
  • Speech
  • Written Text

3. Components of NLP

Basically, there are two components of Natural Language Processing systems:

a. Natural Language Understanding (NLU)

In this, we have to understand the basic tasks −

  • Basically, the mapping to given input in natural language into useful representations.
  • Analyzing different aspects of the language.

b. Natural Language Generation (NLG)

We have to produce meaningful phrases and sentences. That is in the form of natural language from internal representation.

As this process involves:
  • Text planning
In this process, we have to retrieve the relevant content from a knowledge base.
  • Sentence planning
We have to choose the required words for setting a tone of the sentence.
c. Text Realization
Basically, it’s process of mapping sentence plan into sentence structure.
Although, the NLU is harder than NLG.

4. Difficulties in NLU

a. Lexical ambiguity
It’s predefined at a very primitive level such as word-level.
b. Syntax Level ambiguity
In this, we can define a sentence in a parsed way in different ways.
c. Referential ambiguity
Referential ambiguity says that we have to refer something using pronouns only.

5. Natural Language Processing – Terminologies

a. Phonology
It’s study of organizing sound.
b. Morphology 
Basically, it’s study of the construction of words from primitive meaningful units.
c. Morpheme
As we can say that it’s primitive unit of meaning in a language:
  •  Syntax
In this, we have to arrange words to make a sentence. Also, involves determining the structural role of words. That is in the sentence and in phrases.
  • Semantics
It defines the meaning of words. Moreover, how to combine words into meaningful phrases and sentences
  • Pragmatics
It deals with use and understanding sentences in different situations. Also, defines how the interpretation of the sentence is affected.
  • World Knowledge
It includes the general knowledge about the world.

6. Steps in NLP

There are generally five steps in Natural Language Processing:
steps in Natural Language Processing

Steps in Natural Language Processing

a. Lexical Analysis
We have to analyze the structure of words. The collection of words and phrases in a language is a lexicon of a language.
b. Syntactic Analysis (Parsing)
We use parsing for the analysis of the word. Although, have to arrange words in a particular manner. That shows the relationship between words.
 
c. Semantic Analysis
It describes a dictionary meaning which is meaningful. In the task domain, mapping syntactic structures and objects.
d. Discourse Integration
In this step, the meaning of any sentence depends upon the meaning of the previous sentence. In addition. Also brings the meaning to immediately succeeding sentence.
e. Pragmatic Analysis
In this step, data is interpreted on what it actually meant. Although, we have to derive aspects of language which require real-world knowledge.

7. Examples of NLP Systems

Let’s see some example which clears our vision of Natural Language Processing:

a. Customer Review
  • As it’s a most important factor that helps companies to discover relevant information for their business. Further, helps in improving customer satisfaction.
  • As more suggestion comes, it’s more relevant services are better. Also, helps in understanding the customer’s needs.
b. Virtual digital assistants
Virtual digital assistant technologies are currently the most well-known type of artificial intelligence.

8. Why Do We Need NLP?

With this, we can perform certain tasks such as Automated speech and automated text writing in less time.

Moreover, these tasks include too many NLP applications.
For Example:
Automatic Summarization (to generate summary of given text)
Machine Translation (translation of one language into another)

9. The Process of Natural Language Processing

In the NLP process, a text is composed of speech, speech-to-text conversion is performed.

In this mechanism, it involves two processes:
  • Natural Language Understanding
  • Natural Language Generation
Process of Natural Language Processing 

The process of Natural Language Processing

a. Natural Language Understanding (NLU)

We use natural language understanding to learn the meaning of given text. For NLU, we must understand the nature and structure of each word.
i. Lexical Ambiguity
In this, words have multiple meanings
ii. Syntactic Ambiguity
Basically, in this syntactic ambiguity, the sentence having multiple parse trees.
iii. Semantic Ambiguity
Generally, in this, sentence having multiple meanings
iv. Anaphoric Ambiguity
Basically, in this phrase or word are presents. That are previously mentioned but has a different meaning.

b. Natural Language Generation (NLG)

Basically, automatic text produced from structured data. That is in a readable format with meaningful phrases and sentences. Although, the problem of natural language generation is hard to deal with. It is a subset of NLP.

Natural language generation divided into three proposed stages:
i. Text Planning
Basically, it’s ordering of content in structure data.
ii. Sentence Planning
Generally, from structure data, we have to combine sentences to represent the flow of information.
iii. Realization
Basically, to represent text we use a grammatically correct sentence.

10. Applications of NLP

We have few reasons to study natural language processing:
Applications of Natural Language Processing

Applications of Natural Language Processing

a. Communication
  • Basically, a computer is a medium to communicate with users. Also, to learn a new language we can’t force users. Although, for casual users, it’s most important. Such as Managers and children. As they don’t have time and inclination to learn new skills to learn new interaction skills.
  • Basically, in natural language, it’s having a vast store of information. That we have to access via computers. Although, we have to generate information constantly. Also, it’s in the form of books, business, and government report.
  • Generally, in natural language processing, problems of AI arise in a very clear and explicit form.
  • Moreover, there are three major aspects of any natural language understanding theory:
b. Syntax
Basically, we use it to describe the form of the language. Also, grammar is used to specify it. further, we use natural language for the A.I languages of logic and computer programs. Also, these language is more complicated than other formal languages.
c. Semantics
Generally, utterances meaning provided with the of semantics. Although, if we want to build this understanding, general semantic theories exist for it.
d. Pragmatics
Basically, we use this component to explain how the utterances relate to the world.

11. Importance of Natural Language Processing

We can understand the advantage of natural language programming in an easy way as we consider two statements:
Cloud computing insurance should be part of every service level agreement”
“A good S.L.A ensures an easier night’s sleep — even in the cloud.”
Generally, if an individual is used to of NLP, in an entity, a person will recognize cloud computing program. Also, a cloud is an abbreviated form of cloud computing.
Basically, in human language, these type of vague elements appears frequently. Although, machine learning algorithms are historically bad at interpreting. Moreover, many improvements take place in deep learning and artificial intelligence. And interpret them effectively.
So, this was all about AI NLP (Natural Language Processing in Artificial Language). Hope you like our explanation.

12. Conclusion

As a  result, we have studied Natural Language Processing. Also, learned its components, examples and applications. Although, usage of images gives you a better understanding. I hope this blog will help you. Furthermore, if you feel any query, feel free to ask in the comment section.

Reference

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