Raktim Singh

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What is Natural Language Processing

What is Natural Language Processing

Introduction to Natural Language Processing:

Natural Language Processing is a part of Artificial Intelligence that helps computers understand, interpret, analyze and utilize human languages.

In a world where words hold immense power, an extraordinary technology has emerged
that bridges the gap between human language and computational intelligence.
Natural Language Processing (NLP), an enchanting field that unlocks the ability to
understand, interpret, and generate human language using the prowess of machines.

Join us on an exhilarating journey as we delve into the intricacies of NLP, exploring its
fascinating features, historical milestones, real-world applications, and its promising future.

Interesting Things about Human Languages

1. There are more than 7000 languages spoken in the world.
2. Top 10 spoken languages are English, Mandarin, Hindi, Spanish, French, Arabic,
Russian, Portuguese, Bengali & German.
3. Papua New Guinea has four official languages and 850 non-official languages.
4. Though it is not easy to count but it is estimated that English has around 170,000 words
in dictionary, Hindi has around 160,000 words, Russian has around 150,000, Chinese has around 85,000.

Interesting thing about Natural Language Processing

1. Imagine a computer comprehending human language with all its nuances, idioms,
and subtleties! NLP enables machines to understand the complexities of language,
opening doors to seamless communication between humans and computers.

2. With NLP, machines can analyze vast volumes of text, extract valuable information,
and even generate human-like responses, revolutionizing customer service, content
creation, and data analysis.

3. NLP allows us to explore the realm of sentiment analysis, uncovering the emotions,
opinions, and attitudes hidden within written or spoken text. This ability has profound
implications for market research, social media monitoring, and customer feedback

NLP definition:

Natural Language Processing is an enchanting blend of artificial intelligence, linguistics, and
computer science that empowers machines to understand, interpret, and generate human

By utilizing algorithms, statistical models, and linguistic rules,
NLP enables computers to extract meaning, detect sentiment, and respond intelligently to
written or spoken text, bridging the gap between human communication and computational

History of Natural Language Processing:

In 1916, Albert Sechehaye and Charles Bally published ‘ Cours de Linguistique Générale ’
based on the work of Swiss linguistics professor named Ferdinand de Saussure. The book
laid the foundation for the structuralist approach, starting with linguistics.
In 1950, Alan Turing wrote a paper describing a test for a “thinking” machine.
From here, the pioneers of artificial intelligence began exploring the possibility of teaching
machines to understand and process human language.

Lot of work had happened on this between 1950 and 1966.
In 1958, LISP (Locator/Identifier Separation Protocol), the programming language was
released by John McCarthy. In 1964, ELIZA, a “typewritten” comment and response process, was developed.

In 1964, the U.S. National Research Council (NRC) created the Automatic Language
Processing Advisory Committee (ALPAC).
Aim of this was to evaluate the progress of Natural Language Processing research.
But work on NLP stopped by 1966. Reason being that by that time, still machine translation was  costlier than manual translation.

Work on NLP again started in 1980. While in earlier approaches, researchers were mixing
the linguistics and statistics, now the focus was on pure statistics.
Significant milestones include the development of early machine translation systems, the
birth of information retrieval techniques, and the advancement of linguistic theories.
Visionaries such as Alan Turing, Noam Chomsky, John McCarthy and companies like IBM
and Google have played pivotal roles in the evolution of NLP, shaping it into the powerful field it is today.
In the year 2011, Apple released Siri, which is considered as one of the world’s first
successful Natural Language Processing assistants to be used by general consumers.

How Natural Language Processing Works:

NLP operates through a series of sophisticated steps, encompassing tasks such as text
preprocessing, syntactic parsing, semantic analysis, and language generation.

It involves techniques like tokenization (breaking text into meaningful units), part-of-
speech tagging (identifying word types), and sentiment analysis (determining the emotional tone of text).

Natural Language processing differs from the conventional keyword approach. Here
semantic query processing is done.

While in conventional search, AND, OR and NOT is used,
in NLP, algorithm tries to comprehend the relationship between words and the likely intent behind the question or the sentence.

In Natural Language processing, two main techniques namely syntax and semantic analysis is used.

Syntax analysis include parsing, word segmentation, sentence breaking, stemming,
morphological segmentation. Here basically, machine tries to break & extract various
words from a sentence or from a set of sentences.

Semantics involves word sense disambiguation, named entity recognition, natural language
Here machine tries to make meaning of various words derived by Syntax analysis.
In each language, based on the context, same word can convey different meaning.

So, machine tries to arrive at the meaning of various words in the given context.
NLP algorithms, powered by machine learning and deep learning, learn from vast amounts
of data to understand the intricacies of language and make accurate predictions.

If you want to learn about Artificial Intelligence, read my article at


If you want to learn about Machine Learning, read my article at


Application of Natural Language Processing:

1. Machine translation: To translate from one language to another.
2. Text prediction: While writing mail or doing search, many times next word is auto
suggested. This is done with text prediction. This also helps in automatically completing
the sentence and automatically correcting the words or suggesting the correct word.

3. Sentiment Analysis: It is used in market research, public relations, product analytic etc.
4. Text Classification: NLP enables the categorization of text into various classes or
categories, making it invaluable for applications like spam detection, sentiment analysis,
and topic modeling.

5. Spell Check: A spell checker software application identifies and corrects spelling
mistakes in a text.
6. Character recognition: OCR (Optical Character recognition) is the process of converting
images of handwritten, printed text into machine-encoded language.

7. Social Media Monitoring: Companies are using NLP to analyze various social media
posts to get an idea about their products, usage of their product by customer etc.
8. Conversational User Interface: Various chatbot provide an interface, by which human
can interact with machines and get answer for their various queries.

Advantages of Natural Language Processing:

NLP offers a multitude of benefits, including:
1. Enhanced Communication: NLP enables seamless interaction between humans and
machines, making it easier to access information, seek assistance, and perform tasks
through natural language interfaces.

2. Efficiency and Automation: NLP automates language-intensive processes such as text
summarization, information extraction, and document classification, saving time and resources.
3.Actionable Insights: By analyzing text data, NLP uncovers valuable insights, trends,
and patterns, empowering organizations to make data-driven decisions and gain a competitive edge.

Use Cases of Natural Language Processing:

1. Virtual Assistants: Platforms like Siri, Alexa, and Google Assistant rely on NLP to
understand spoken commands, answer questions, and perform tasks, making our lives more convenient and interactive.

2. Chatbots: NLP powers chatbots, allowing them to engage in human-like conversations,
provide customer support, and automate repetitive tasks in various industries.

3.Language Translation: Online translation services like Google Translate leverage NLP
algorithms to instantly translate text between multiple languages, facilitating communication across borders.

Companies Using Natural Language Processing:

1. Amazon: Amazon employs NLP in its voice-activated assistant, Alexa, enabling users to
interact with their devices using natural language commands.
2. Microsoft: Microsoft utilizes NLP in applications like Microsoft Office and Azure Cognitive
Services to enhance productivity, enable language understanding, and extract insights from text data.

3.OpenAI: OpenAI, known for its cutting-edge language models, harnesses NLP
techniques to create advanced AI systems capable of generating human-like text and engaging in meaningful conversations.

Industries Using Natural Language Processing:

1. Customer Service: NLP powers chatbots and virtual assistants in customer service,
streamlining interactions and providing personalized support.
2. Healthcare: NLP helps analyze medical records, extract information, and assist in
clinical decision-making, improving patient care and enabling medical research.

3.Media and Entertainment: NLP enables content recommendation systems, sentiment
analysis of social media buzz, and automated content creation, enhancing the user experience.

Apart from that Industries such as legal services, education, e-commerce, and market
research can greatly benefit from NLP. It can automate legal document analysis, enhance
language learning platforms, improve product reviews and sentiment analysis, and provide
valuable market insights.

Related Technologies:

To fully appreciate the capabilities of NLP, it's important to understand related
technologies such as Machine Learning, Deep Learning, Speech Recognition, and Information Retrieval.

These synergistic fields complement NLP and enable the development of powerful language-driven application.

What Natural Language Processing Doesn’t Contain:

NLP is a fascinating domain, but its important to note its limitations.
NLP doesn’t encompass human-level comprehension, emotional intelligence, or context understanding to the same extent as humans.
It is an evolving field that continually strives to bridge the gap between
human language and machine understanding.

When Not to Use Natural Language Processing:

NLP may not be suitable in scenarios where the language is highly domain-specific or lacks
sufficient training data. In such cases, specialized rule-based systems or expert knowledge
may yield better results.

Future Evolution of Natural Language Processing:

The future of NLP is awe-inspiring, with advancements in areas like contextual understanding,
emotion detection, and multi-modal language processing. NLP will continue to play a pivotal role in technologies like voice-controlled smart devices, personalized digital assistants, and human-like conversational AI.


Natural Language Processing has opened the doors to a world where machines comprehend
and interact with human language, transforming the way we communicate, make
decisions and extract knowledge from vast amounts of textual data.

Its historical journey, remarkable features, and vast applications make NLP an essential component of our increasingly connected world.

As we embark on this enthralling adventure into the language of machines, we witness the
fusion of human intelligence and computational capabilities, paving the way for a future
where language is no longer a barrier but a bridge. So, join us on this captivating journey
as we unlock the power of Natural Language Processing, unraveling the mysteries of the spoken and written word.

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