Natural Language Processing in Artificial Intelligence in Hindi | NLP with Demo and Examples

Summary

This video delves into the realm of Natural Language Processing (NLP), a pivotal branch of artificial intelligence that enables computers to understand, interpret, and generate human language. With practical examples, the speaker elucidates the significance of NLP in various applications such as speech recognition, sentiment analysis, machine translation, and chatbots. The video also explores the intricacies of NLU (Natural Language Understanding), highlighting the challenges of ambiguity in language interpretation and the importance of context. It concludes with insights into NLG (Natural Language Generation), emphasizing the need for intelligent and contextually relevant responses in AI communication.

Takeaways

• 💡 Natural Language Processing (NLP) is a vital branch of artificial intelligence that enables computers to understand, interpret, and generate human language.
• 🗣️ Human communication relies on natural language, and NLP aims to replicate this ability in machines, allowing them to communicate effectively with humans.
• 📈 NLP has a wide range of applications, including speech recognition, sentiment analysis, machine translation, and chatbots, which are utilized by major tech companies like Google and Amazon.
• 🔍 Speech recognition technology allows devices like Google Assistant and Siri to interpret spoken language and provide appropriate responses.
• 📊 Sentiment analysis is used to gauge public opinion on social media platforms, helping to analyze the positive or negative sentiments towards various topics, such as movie ratings or political stances.
• 🌐 Machine translation services, like Google Translate, facilitate communication by instantly translating text from one language to another, breaking down language barriers.
• 🤖 Chatbots are AI-powered programs that simulate conversation with users, providing automated responses based on their knowledge base, enhancing customer service and user engagement.
• 🧠 The process of NLP involves several stages: Natural Language Understanding (NLU), where the machine interprets the user's intent, and Natural Language Generation (NLG), where the machine formulates a response.
• 🔎 NLU faces challenges such as lexical, syntactic, semantic, and pragmatic ambiguity, which require sophisticated algorithms to resolve and ensure accurate interpretation of language.
• 💬 NLG focuses on creating contextually relevant and intelligent responses that are structured and meaningful, ensuring the machine's reply aligns with the user's query and maintains a natural conversation flow.

Q & A

What is Natural Language Processing (NLP)?

Natural Language Processing is a sub-field of computer science, information engineering, and artificial intelligence that is concerned with the interactions between computers and human languages, aiming to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful.

Why is NLP important for competitive exams and interviews?

NLP is important for competitive exams and interviews because it is a rapidly growing field in artificial intelligence with numerous applications in technology and business. Understanding NLP can give candidates an edge in exams and interviews, showcasing their knowledge of current technological trends and their potential to work with cutting-edge AI systems.

What are the primary applications of NLP mentioned in the script?

The primary applications of NLP mentioned in the script include speech recognition, sentiment analysis, machine translation, and chatbots. These applications are used in various industries such as IT, customer service, social media, and language translation services.

Can you provide an example of speech recognition technology?

An example of speech recognition technology is Google Assistant and Siri by Apple. These systems can interpret spoken language in various languages, understand the user's query, and provide a relevant response.

How does sentiment analysis work on social media platforms?

Sentiment analysis on social media platforms like Facebook and Twitter involves analyzing text from posts or tweets to determine the sentiment or opinion expressed, such as whether the comments are positive or negative. This can be used for gauging public opinion on various topics, like movie ratings or political stances.

What is machine translation and how does it help in communication?

Machine translation is the process by which a computer program translates text or speech from one language to another. It helps in communication by allowing people who speak different languages to understand each other without needing a human interpreter, making it easier to communicate across language barriers.

What is a chatbot and how does it function?

A chatbot is a software application that uses AI to conduct conversations with users in natural language. It functions by processing user inputs, understanding the intent behind the queries, and generating appropriate responses from its knowledge base to simulate a conversation with a human.

What are the challenges faced by NLP in understanding human language?

The challenges faced by NLP in understanding human language include lexical ambiguity, syntactic ambiguity, semantic ambiguity, and pragmatic ambiguity. These challenges arise from the multiple meanings of words, sentence structures, contextual meanings, and the interpretation of phrases based on the situational context.

How does NLP handle ambiguity in language?

NLP handles ambiguity in language through various techniques such as tokenization, parsing, lemmatization, stemming, and named entity recognition. These methods help in breaking down the text, understanding the context, and differentiating between various meanings to provide a more accurate interpretation.

What is the role of Natural Language Generation (NLG) in NLP?

Natural Language Generation (NLG) in NLP is responsible for generating human-like text based on the understanding achieved through Natural Language Understanding (NLU). It involves text and syntax planning, using a knowledge base to construct coherent and contextually relevant responses to user queries.

How does structured data play a role in NLG?

Structured data in NLG plays a role by providing a well-organized and easily accessible knowledge base from which the system can select and arrange information to generate responses. This ensures that the generated text is not only coherent but also relevant and structured in a way that is easy for users to understand and utilize.

Outlines

00:00

💬 Introduction to Natural Language Processing

The speaker begins by introducing the topic of Natural Language Processing (NLP), emphasizing its significance in artificial intelligence. NLP aims to enable computers to understand, interpret, and generate human language in a manner similar to how humans communicate. The video promises to delve into NLP with practical examples, catering to students, exam takers, and professionals preparing for interviews. The speaker highlights the importance of language in human interaction and the goal of replicating this in machines for effective communication. Applications of NLP discussed include speech recognition, with examples like Google Assistant and Siri, sentiment analysis in social media, and machine translation services like Google Translate.

05:02

🤖 Applications and Challenges of NLP

This paragraph explores various applications of NLP, such as chatbots, advertisement matching, and spell-check software. The speaker uses Google Assistant as an example to illustrate the process of NLP, which includes automatic speech recognition, natural language understanding (NLU), and natural language generation (NLG). The video aims to explain how these components work together to process and generate human-like responses. The speaker also touches on the challenges faced in NLP, particularly the issue of ambiguity in language, which includes lexical, syntactic, semantic, and pragmatic ambiguities. These challenges are crucial for NLP systems to overcome to accurately interpret and respond to human language.

10:09

🧠 Deep Dive into NLP: Ambiguities and Solutions

The speaker delves deeper into the challenges of NLP, focusing on the types of ambiguities that machines face when interpreting human language. Lexical ambiguity refers to words with multiple meanings, syntactic ambiguity pertains to sentence structure that can be interpreted in different ways, and semantic ambiguity involves the multiple possible meanings of a phrase. Pragmatic ambiguity is also discussed, which is about the context-dependent interpretations of language. The paragraph explains how NLP software must be intelligently designed to handle these ambiguities, often using techniques like tokenization, parsing, and understanding the context to provide accurate responses.

15:10

📚 Conclusion: NLP's Role in Structured Data and Communication

In the final paragraph, the speaker wraps up the discussion on NLP by emphasizing its role in structured data and relevant communication. The speaker explains that after understanding the user's input through NLU, the system must generate a response that is both intelligent and contextually appropriate, which is where NLG comes into play. The paragraph also highlights the importance of structured data in providing clear and organized responses to users, such as weather forecasts. The speaker concludes by summarizing the key points of NLP, including its recognition and interpretation of speech and text, and thanks the audience for their attention.

Keywords

💡Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human languages. It aims to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. In the video, NLP is the central theme, with the speaker discussing its importance in various applications such as speech recognition, sentiment analysis, and machine translation. The video aims to provide an overview of NLP, its applications, and the challenges it faces.

💡Speech Recognition

Speech recognition is a technology that enables computers to understand and interpret spoken language, converting it into written text or actionable commands. The video mentions Google Assistant and Apple's Siri as prominent examples of speech recognition, highlighting how users can interact with these systems using natural language to ask questions or give commands.

💡Sentiment Analysis

Sentiment analysis, also known as opinion mining, is the process of determining whether a piece of writing is positive, negative, or neutral. In the context of the video, the speaker discusses how sentiment analysis is widely used on social media platforms like Twitter and Facebook to gauge public opinion on various topics, such as movie ratings or political stances.

💡Machine Translation

Machine translation is a type of NLP that automatically translates text or speech from one language to another. The video provides Google Translate as an example, illustrating how it can help users understand comments in languages they do not know, thus facilitating communication across language barriers.

💡Chatbots

Chatbots are computer programs designed to simulate conversation with human users. They are typically used in customer service and support, providing automated responses to common questions. The video explains chatbots as a means of communication where the user interacts with a machine that replies based on its knowledge base, rather than a human.

💡Natural Language Understanding (NLU)

Natural Language Understanding is the ability of a system to interpret and understand the meaning behind spoken or written language. It involves recognizing the intent, context, and semantics of user inputs. In the video, NLU is described as a critical component of NLP, where the system must comprehend the user's language to provide appropriate responses.

💡Natural Language Generation (NLG)

Natural Language Generation is the process by which a system converts its understanding of data into human-readable language. It involves creating text or speech that is coherent, contextually relevant, and meaningful to the user. The video emphasizes the importance of NLG in ensuring that the system's responses are not only intelligible but also contextually appropriate.

💡Ambiguity

Ambiguity in NLP refers to the multiple possible interpretations of a word or phrase, which can lead to confusion for language processing systems. The video discusses various types of ambiguity, such as lexical, syntactic, semantic, and pragmatic, and how they challenge the accuracy of NLP systems. The speaker provides examples to illustrate how these ambiguities can affect the interpretation of language by machines.

💡Tokenization

Tokenization is the process of breaking down text into individual elements or tokens, which are typically words or phrases. This is a fundamental step in many NLP tasks, including sentiment analysis and machine translation. The video uses the example of 'How are you?' being tokenized into separate words to explain how this process is a precursor to understanding the language's structure and meaning.

💡Corpus

A corpus is a large and structured set of texts that serves as a resource for linguistic research and NLP applications. In the video, the speaker mentions the corpus as the database or knowledge base that NLG systems use to plan and generate responses. The corpus provides the system with the necessary linguistic resources to construct meaningful and contextually appropriate replies.

Highlights

Introduction to Natural Language Processing (NLP) as a vital topic in artificial intelligence.

NLP's significance in competitive exams, college/university exams, and interviews.

Definition of NLP and its goal to replicate human communication for machine interactions.

Applications of NLP including speech recognition, sentiment analysis, machine translation, and chatbots.

Speech recognition technology exemplified by Google Assistant and Apple's Siri.

Sentiment analysis in social media, particularly on platforms like Facebook and Twitter.

Machine translation services like Google Translate facilitating cross-language communication.

Chatbots' role in automating customer service and their operation without human intervention.

The process of NLP involving Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Natural Language Generation (NLG).

Challenges in NLU including lexical, syntactic, semantic, and pragmatic ambiguity.

Lexical ambiguity and its impact on understanding the context of words.

Syntactic ambiguity and the importance of sentence structure in meaning.

Semantic ambiguity and the quest to understand the true meaning behind phrases.

Pragmatic ambiguity and the多重 interpretation of phrases based on context.

Natural Language Generation's focus on creating intelligent and contextually relevant responses.

The importance of structured data in NLG for providing organized and relevant information.

Summary of the basic introduction to natural language processing.

Transcripts

Hello friends!

In this video, we are going to discuss natural language processing.

NLP is one of the most important and latest topics of artificial intelligence.

In this video, we will discuss NLP in detail

with live examples.

This video will help you a lot

especially in competitive exams

or if you are preparing for your college/university-level exam

or even for interviews.

I am going to share very good facts about NLP.

Let's start with the definition of NLP.

How do human beings communicate with each other?

They use their natural language.

I am communicating with you. Which language am I using?

I am using English. I am using Hindi.

I may use some Punjabi words too.

I am using a mixture of 2-3 languages

and you are listening and interpreting all the sentences,

then you understand them, and then you reply accordingly.

That's full-way communication.

This communication is only possible

when you are understanding my language and sending your reply to me

and I am able to understand your reply.

This makes human behaviour very intelligent

that how we communicate through natural languages.

And the computer should replicate the same thing.

And we want the computer to replicate the same thing

and if two computers need to communicate with each other,

they should communicate like human beings.

The machine and human beings may communicate

like two human beings communicate with each other.

We want to implement the same with NLP, i. e. Natural Language Processing.

First, I want to tell the applications.

What are the applications of NLP?

The first is speech recognition.

It is the most widely used.

And the most research work...

All the major IT giants like Google and Amazon are working on this only.

The biggest example of speech recognition is Google Assistant.

You must have also heard about Siri, which is a product of Apple.

What do you do with Google Assistant and Siri?

You ask a question.

Using the English language or the Hindi language,

as of now, it's not working with all languages,

but if you ask a question in the English language, you give it a speech.

It interprets and understands that speech and gives you a valid reply.

That is called speech recognition.

Then, sentiment analysis.

This is also being done widely.

Especially if we talk about social media.

Facebook and Twitter.

All the tweets can be analyzed.

Sentiments mean whether those tweets are good or bad.

For example, the ratings of the movies.

Whether the movie is good or bad.

You can analyze and check this through Twitter also.

During elections, you can check the tweets of any leader and analyze

what they focusing on.

I gave its example a few days back.

By using Python and Tensorflow, in R programming, I analyzed

that how we can analyze using tweets

and create a word cloud through which we can find out

which leader is focusing the most on what topic in their tweets.

This is sentiment analysis.

Machine translation.

A simple example of machine translation is Google Translate.

I don't know the Chinese language.

I especially use this for my comments too.

I get so many comments in which...

I know only 2-3 languages including Hindi and English.

But sometimes people comment in Bangla and Tamil languages.

So I simply copy and paste it

to Google and I get to know its meaning in Translate.

And I reply to them in the same way.

That is called machine translation.

See, how easy it makes communication.

If you...

Earlier, the same was done by interpreters.

If you are visiting a foreign country and you don't know the language of that country.

Then obviously, you will have to hire an interpreter and pay him.

First, you will speak and he will understand you.

And he may not be able to give a 100% valid answer.

Because he is a human being.

And when there are human beings, there are chances of making mistakes.

But if you give the same task to a machine,

the machine will do it as-it-is

if you do it with proper training and experience.

The more proper you make this, the more valid your answer will be.

Then, chatbots.

There are eBay chatbots.

What are chatbots?

Generally, when we chat, there are human beings on both sides.

But nowadays, there are smart chatbots.

when you ask a question, there is no human being but a machine replying to you.

The machine replies to you from its complete database or knowledge base.

It tries to give a valid reply.

That is a chatbot.

This is the main example.

There are more examples like advertisement matching,

that how we get recommendations on the basis of the advertisement history.

There so many softwares for spell-check.

That how to check spellings in the whole text speech.

These are some examples that you can easily find.

But I want to explain NLP to you with a simple example.

First, we are giving input

and the automatic speech recognition software will take that speech

and convert it to text.

NLU is Natural Language Understanding

or Natural Language Interpreter.

The same thing.

I am speaking from here. So you are listening to everything.

As you are listening, your interpreter is working simultaneously.

The understanding is working.

You are collecting all the words and trying to understand them.

Then, what do you do? Natural Language Generation.

On the basis of that, you decide what to reply to it.

Your reply must be valid too.

I will explain NLU and NLG in detail.

I want to explain to you how it works through a simple demo.

Simply, I am using Google Assistance here.

Hey Google!

Google Assistant: Hi! What can I do for you?

How are you?

Google Assistant: I am doing great. Thanks for asking!

Google Assistant: What can I help you with?

What is natural language processing?

Google Assistant: Here's a summary from Wikipedia.

Google Assistant: Natural language processing is a sub-field

Google Assistant: of computer science information engineering

Google Assistant: and artificial intelligence concerned with the interactions

Google Assistant: between computers and human languages.

See, what did we do here?

The words were very simple.

I spoke some words or sentences

and Google Assistant recognized it.

It recognized my words automatically.

Then comes understanding.

Understanding means it collected all those words and understood what I said.

Means what I wanted to say? What's my intention?

And it will try to reply on the basis of my intention.

I want to explain it to you with a simple example.

What's natural language understanding?

"What do the users say?"

Menas what is their intention? What is the meaning?

What do they want to say? What's their intention?

This is actually a challenging task.

This is one of the AI hard problems.

It seems easy.

Like it is tokenizing and understanding what I am saying.

No, there are so many challenges.

And the biggest challenge is ambiguity.

It means that the language you are using,

let's say, I am using the English language.

There are so many words. There is the ambiguity of so many types.

How to eradicate that ambiguity?

This is the biggest challenge for the machine.

The NLU software faces so many challenges.

The first is lexical ambiguity.

Then, syntactic ambiguity.

Semantic. Pragmatic.

This is a simple thing.

In a compiler,

when we input a C program,

the compiler does lexical analysis,

then it checks for syntactic and semantic errors.

What does lexical ambiguity mean?

What does 'lexical' mean?

The words that we are speaking...

For example, I said, "How are you?"

So, "How are you?" will be tokenized.

Tokenize means that it will be converted to different words.

When it will be converted into different words, there may be multiple meanings of the same word.

Let me explain with a simple example. "The tank was full of water."

The tank was full of water.

It can be understood that it was full of water.

But what's 'tank' here? The water tank that we use at home or the army tank?

So, 'tank' is representing multiple meanings.

My answer should be in the context of the question.

This is a big ambiguity challenge for NLU.

The next is syntactic ambiguity.

With syntactic ambiguity, we discuss the structure.

When we pass any sentence, the structure of the sentence must be valid.

If there's a problem with that, there will be problems with the structure.

"Old men and women were taken to the safe place."

Old men and women were taken to the safe place.

Here, 'Old man and women'...

Means, old women and old...

Means, old men, old women.

Is it considering both as old?

Are the men and women both old?

Or just the women are old, but the men are not.

There is certain ambiguity.

That is old men and women.

This is easy to understand for a human being

because we also use intuition and sentiments.

We use some intuitions.

We may think that by 'old men and women', it meant only old women.

But for machines, it is not possible to think like this.

Machines do not have brains.

We want to make them intelligent artificially.

From the point of view of a human being, it is valid.

But if you are giving to a machine, you will have to do it properly.

That is 'Old men and old women were taken to the safe place.'

If you want to remove ambiguity.

Then, we have semantic.

Semantic is related to the meaning.

What's the meaning of it?

For example, "The car hit the pole while it was moving."

What's here, actually?

"Car hit the pole while it was moving."

You get ambiguity in the meaning.

What was moving? Was the moving? Was the pole moving?

There is an ambiguity in the meaning.

In Hindi, a moving car hit a child.

Or you can also say it like 'The car hit the child when it was moving.'

Here, what was moving? The car, or the child?

There is an ambiguity related to the meaning.

Removing this ambiguity is the biggest challenge.

To do so, we use many lemenisations, stemming, and name entity relations.

For example, in Hindi, we will convert all these words.

We will have to divide divided them into nouns, adverbs, objectives, verbs, etc.

After tokenizing and parsing them, we will be able to differentiate between noun, adverb, verb, etc.

In noun too, we have different types of it.

You will have to divide them further,

then you will get its context and the meaning of this.

Then, pragmatic.

Pragmatic is also a challenging ambiguity.

In this, the context of the phrase gives multiple interpretations.

You wrote a word or a sentence that has multiple interpretations.

For example, "The police are coming."

Means, the police are coming.

For whom they are coming? For me? For you?

Or are they coming in this area? Why are they coming?

Actually, there are multiple meanings.

As human beings, as I said earlier,

we will use sentiments, intuitions, and our past experience.

The police are coming, they may be coming to this area.

But if you are giving the same language to a machine,

how will the machine interpret it?

Are they coming for me, you, in this area or they are just coming in this area? Why are they coming?

If you want to remove this ambiguity,

then the NLU software must be very intelligent.

Actually, these are the main challenges in NLU.

The phrase after NLU is NLG.

That is Natural Language Generation.

As we are having complete understanding,

as our mind has a complete understanding, what do we need to do next?

Now we have to reply.

The reply...

What should we say to the user?

Here, we work on the analogy that what to reply to the user now.

Because the reply must be intelligent and conversational.

What you are replying must be intelligent and conversational.

Your reply must be in the context of what the user has asked.

For example, the user asked Google Assistant,

"Google Assistant, tell me the nearby places to eat."

And the machine replies about the nearby places for cloth shopping.

I am getting the answer related to cloth shopping.

But I asked about the food.

Obviously, this answer is irrelevant.

Natural language generation eradicates this thing.

In natural language generation, we do text and syntax planning.

We have the whole corpus.

Corpus is the whole database.

It is the complete knowledge base.

From that knowledge base, we plan the words.

On the basis of that, we plan the sentences.

After planning the sentences, we plan the structure.

Structure means that we deal with structured data.

Let me explain structured data with a simple example.

If you check how will be the weather the next week online,

you will get a structure-based reply.

There will be a box in which the weather for each day will be mentioned.

There will also be a summary to analyze it.

It replies to you with structured data.

And this reply must be relevant to the question asked by the user.

This whole system comes under natural language processing.

It recognizes the text or the speech that you are giving.

These are the two main factors, NLU, and NLG.

This is all about the basic introduction of natural language processing.

Thank you!