Natural Language Processing: What You Must Know


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Since language is a means of communication for humans, by studying language we can understand more about the world. Natural Language Processing is a technique of computer analysis of human language (Natural Language) in the form of input and conversion of it into a useful form of representation. Thus, Natural Language Processing is a procedure used to help computers understand the natural languages of humans. Natural Language Processing is one of the fields of Artificial Intelligence that processes or analyses written or spoken languages for speech, grammar and meaning.
It’s not an easy task to understand how we communicate with computer with our own natural languages. In this article you get to know about the basic introduction to natural language processing and how we can achieve that.

What Is Natural Language Processing (NLP)?

Natural language processing, commonly known as NLP, is a branch of Artificial Intelligence that deals with the communication between computers and humans with their own natural language. The ultimate aim of NLP is to read and understand the value of human languages. Most NLP techniques rely on machine learning algorithms to extract meanings from human languages.
Natural Language Processing consists of two parts: Natural Language Understanding (NLU) and Natural Language Generation (NLG). To understand any language, we require detailed knowledge of that language and linguistic facts. In fact, a specific interaction between humans and machines using natural language processing can be as follows:

1. A human talks to a machine
2. The machine captures audio
3. Audio to Text Conversion takes place
4. Processing of textual data
5. Data is converted into audio
6. Machine responds to human by playing that audio files

What Is Natural Language Processing (NLP) Used For?

Natural language processing is the core concept behind the following common applications:

Language translation applications like Google Translate
Word processors such as Microsoft Word and Grammarly that employ NLP to check the grammatical accuracy of texts.
Interactive Voice Response (IVR) applications are used in call centres to respond to the requests of some users.
Personal Assistant applications such as Alexa, Siri, OK Google and Cortana.

Why Natural Language Processing (NLP) Is Difficult?

Due to the nature of human languages, Natural Language Processing is still considered one the difficult tasks in computer science. Rules that determine the meaning (sense) of information content in Natural Languages of humans are not easy for computers to understand.
Some of these rules can be high-level and abstract; For example, when a person uses sarcastic remarks to pass information. On the other hand, some of these rules may be low-level; For example, using the character “s” to denote a multiplicity of objects.

To understand human language comprehensively requires understanding both words and how concepts are interlinked to create the intended message. While a human can easily master a language, the ambiguity and impenetrable features of natural languages are such that make it difficult for machines to implement NLP.

How Does Natural Language Processing Work?

When the text has been provided, the computer will use an algorithm to extract the meanings associated with each sentence and collect the necessary data from them. NLP concentrates on applying algorithms to identify and extract natural languages rules, so that unstructured language data can be transformed into a form that can be understood by computers.
Sometimes, a computer may fail to understand the meaning of a sentence correctly, which can cause delivery of wrong information.

What Are The Techniques Used In Natural Language Processing (NLP)?

NLP Steps/ Processes

Natural Language Processing Block Diagram

NLP is composed of two parts: Natural Language Understanding (NLU) and Natural Language Generation (NLG). Synthetic analysis and semantic analysis are the main techniques used to carry out natural language processing tasks. A complete NLP system consists of programs that perform all these functions.
The description of every steps (block) in the NLP process is as follows:

1. Input/Source:

The input to NLP system can be written text or speech. The quality of input decides the possible errors in language processing. That means, high quality input leads to more accurate language understanding.

2. Segmentation:

Thus obtained inputs are divided into segments (Chunks) and those segments are analysed. Each such chunk is called frames.

3. Syntactic Analysis:

Syntactic analysis produces grammatical structure representation by taking an input sentence. Syntax refers to the arrangement of words in a sentence such that they make grammatical meaning.

In NLP, syntactic analysis is used to assess how natural language aligns with grammatical rules. Computer algorithms help to apply grammatical rules to a group of words and deduce meaning from them.

Here some syntax techniques can be used:


It emphasizes the reduction of different dividing forms of a word into a single form for easy analysis.

Morphological segmentation:

It involves splitting the words into separate units, called morpheme.

Word segmentation:

This involves dividing a large piece of continuous text into separate units.

Part-to-speech tagging:

This involves identifying part of speech for every word.


This involves performing a grammatical analysis for an available sentence.

Sentence breaking:

This involves placing sentence boundaries on a large piece of text.


This involves cutting off the split words in their original form.

A grammar describes the valid parts of speech in a language and ways to combine them into phrases. The grammar of English is almost context free.


A computer grammar stipulates the sentences that are in a language and their parse trees. A parse tree is a hierarchical structure which shows how the grammar can be applied to the input. One grammar rule corresponds to the application of each level of tree.

4. Semantic Analysis:

Semantic analysis is a process of transforming the syntactic representations into a meaning representation. Semantics refers to the meaning that is expressed by a text. Semantic analysis is one of the difficult aspects of natural language processing that has not yet been fully resolved.

It involves applying computer algorithms to understand the meaning and interpretation of words and structure of sentences.

Here are some techniques in semantic analysis:

Named entity recognition (NER):

This involves determining the parts of a text that can be identified and classified into predetermined groups.

Examples of such groups include people’s names and place names.

Breaking word perception:

It involves giving meaning to a word based on context.

Natural language generation:

This involves using databases to derive semantic intentions and convert them into human language.

Word sense determination:

Words have different meanings in different contexts.

For example: John had a bat in his office.

bat = “a cricket thing”

bat = “a flying mammal”

Sentence level analysis:

The sentence must be assigned some meaning once the words are understood.

For example: She saw her duck.

Colourless green sleep anxiously. -> This shall be rejected semantically as colourless green would make no sense.

5. Pragmatic Analysis:

Pragmatic comprises aspects of meaning that depends upon the context or upon facts about real world. These aspects include:

Pronouns and referring expressions

Logical inferences that can be drawn from the meanings of a set of propositions.

Discourse Structure:

The meaning of a collection of sentences when taken as combined.

Lexicon or dictionary

Morphological Analysis System

Natural Language Analysis Techniques

The commonly used techniques in Natural Language Processing are:

Keyword Analysis or Pattern Matching

Accept the given sentence as input.

Segment the sentence.

Identify the keywords in each segment.

If keyword is present:

If only one keyword is present, give suitable reply similar to keyword

If more than one keywords are present, prioritize and give suitable reply as of keyword

If no keyword is present in the segment, give a random reply.

Syntactic driven parsing technique

Words can fit together in higher level units such as phrases, clauses and sentences.

Interpretations of larger groups of words are built up out of the interpretation of their syntactic constituent words or phrases.

Interpretation i/p done as a whole.

Obtained by application of grammar that determines the sentences which are legal in the language that is being parsed.



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