Natural Language Processing | Artificial Intelligence for Class 10 PDF Download

 Page 1


 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Natural Language Processing 
Introduction 
Till now, we have explored two domains of AI: Data Science and Computer Vision. Both these domains 
differ from each other in terms of the data on which they work. Data Science works around numbers 
and tabular data while Computer Vision is all about visual data like images and videos. The third 
domain, Natural Language Processing (commonly called NLP) takes in the data of Natural Languages 
which humans use in their daily lives and operates on this. 
Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to 
understand and process human languages. AI is a subfield of Linguistics, Computer Science, 
Information Engineering, and Artificial Intelligence concerned with the interactions between 
computers and human (natural) languages, in particular how to program computers to process and 
analyse large amounts of natural language data. 
But how do computers do that? How do they understand what we say in our language? This chapter 
is all about demystifying the Natural Language Processing domain and understanding how it works. 
Before we get deeper into NLP, let us experience it with the help of this AI Game: 
 
Identify the mystery animal: http://bit.ly/iai4yma  
 
 
Go to this link on Google Chrome, launch the experiment and try to identify the Mystery Animal by 
asking the machine 20 Yes or No questions. 
Were you able to guess the animal? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If yes, in how many questions were you able to guess it? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If no, how many times did you try playing this game? 
__________________________________________________________________________________
__________________________________________________________________________________ 
What according to you was the task of the machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
  
Page 2


 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Natural Language Processing 
Introduction 
Till now, we have explored two domains of AI: Data Science and Computer Vision. Both these domains 
differ from each other in terms of the data on which they work. Data Science works around numbers 
and tabular data while Computer Vision is all about visual data like images and videos. The third 
domain, Natural Language Processing (commonly called NLP) takes in the data of Natural Languages 
which humans use in their daily lives and operates on this. 
Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to 
understand and process human languages. AI is a subfield of Linguistics, Computer Science, 
Information Engineering, and Artificial Intelligence concerned with the interactions between 
computers and human (natural) languages, in particular how to program computers to process and 
analyse large amounts of natural language data. 
But how do computers do that? How do they understand what we say in our language? This chapter 
is all about demystifying the Natural Language Processing domain and understanding how it works. 
Before we get deeper into NLP, let us experience it with the help of this AI Game: 
 
Identify the mystery animal: http://bit.ly/iai4yma  
 
 
Go to this link on Google Chrome, launch the experiment and try to identify the Mystery Animal by 
asking the machine 20 Yes or No questions. 
Were you able to guess the animal? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If yes, in how many questions were you able to guess it? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If no, how many times did you try playing this game? 
__________________________________________________________________________________
__________________________________________________________________________________ 
What according to you was the task of the machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Were there any challenges that you faced while playing this game? If yes, list them down. 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach must one follow to win this game? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Natural Language Processing 
Since Artificial Intelligence nowadays is becoming an integral part of our lives, its applications are very 
commonly used by the majority of people in their daily lives. Here are some of the applications of 
Natural Language Processing which are used in the real-life scenario: 
Automatic Summarization: Information overload is a real 
problem when we need to access a specific, important piece 
of information from a huge knowledge base. Automatic 
summarization is relevant not only for summarizing the 
meaning of documents and information, but also to 
understand the emotional meanings within the information, 
such as in collecting data from social media. Automatic 
summarization is especially relevant when used to provide an 
overview of a news item or blog post, while avoiding 
redundancy from multiple sources and maximizing the 
diversity of content obtained. 
Sentiment Analysis: The goal of sentiment 
analysis is to identify sentiment among several 
posts or even in the same post where emotion is 
not always explicitly expressed. Companies use 
Natural Language Processing applications, such as 
sentiment analysis, to identify opinions and 
sentiment online to help them understand what 
customers think about their products and services 
(i.e., “I love the new iPhone” and, a few lines later 
“But sometimes it doesn’t work well” where the 
person is still talking about the iPhone) and overall 
Page 3


 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Natural Language Processing 
Introduction 
Till now, we have explored two domains of AI: Data Science and Computer Vision. Both these domains 
differ from each other in terms of the data on which they work. Data Science works around numbers 
and tabular data while Computer Vision is all about visual data like images and videos. The third 
domain, Natural Language Processing (commonly called NLP) takes in the data of Natural Languages 
which humans use in their daily lives and operates on this. 
Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to 
understand and process human languages. AI is a subfield of Linguistics, Computer Science, 
Information Engineering, and Artificial Intelligence concerned with the interactions between 
computers and human (natural) languages, in particular how to program computers to process and 
analyse large amounts of natural language data. 
But how do computers do that? How do they understand what we say in our language? This chapter 
is all about demystifying the Natural Language Processing domain and understanding how it works. 
Before we get deeper into NLP, let us experience it with the help of this AI Game: 
 
Identify the mystery animal: http://bit.ly/iai4yma  
 
 
Go to this link on Google Chrome, launch the experiment and try to identify the Mystery Animal by 
asking the machine 20 Yes or No questions. 
Were you able to guess the animal? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If yes, in how many questions were you able to guess it? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If no, how many times did you try playing this game? 
__________________________________________________________________________________
__________________________________________________________________________________ 
What according to you was the task of the machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Were there any challenges that you faced while playing this game? If yes, list them down. 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach must one follow to win this game? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Natural Language Processing 
Since Artificial Intelligence nowadays is becoming an integral part of our lives, its applications are very 
commonly used by the majority of people in their daily lives. Here are some of the applications of 
Natural Language Processing which are used in the real-life scenario: 
Automatic Summarization: Information overload is a real 
problem when we need to access a specific, important piece 
of information from a huge knowledge base. Automatic 
summarization is relevant not only for summarizing the 
meaning of documents and information, but also to 
understand the emotional meanings within the information, 
such as in collecting data from social media. Automatic 
summarization is especially relevant when used to provide an 
overview of a news item or blog post, while avoiding 
redundancy from multiple sources and maximizing the 
diversity of content obtained. 
Sentiment Analysis: The goal of sentiment 
analysis is to identify sentiment among several 
posts or even in the same post where emotion is 
not always explicitly expressed. Companies use 
Natural Language Processing applications, such as 
sentiment analysis, to identify opinions and 
sentiment online to help them understand what 
customers think about their products and services 
(i.e., “I love the new iPhone” and, a few lines later 
“But sometimes it doesn’t work well” where the 
person is still talking about the iPhone) and overall 
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
indicators of their reputation. Beyond determining simple polarity, sentiment analysis understands 
sentiment in context to help better understand what’s behind an expressed opinion, which can be 
extremely relevant in understanding and driving purchasing decisions. 
 
Text classification: Text classification makes it possible to assign 
predefined categories to a document and organize it to help you 
find the information you need or simplify some activities. For 
example, an application of text categorization is spam filtering in 
email. 
 
 
Virtual Assistants: Nowadays Google Assistant, Cortana, 
Siri, Alexa, etc have become an integral part of our lives. Not 
only can we talk to them but they also have the abilities to 
make our lives easier. By accessing our data, they can help 
us in keeping notes of our tasks, make calls for us, send 
messages and a lot more. With the help of speech 
recognition, these assistants can not only detect our speech 
but can also make sense out of it. According to recent 
researches, a lot more advancements are expected in this 
field in the near future. 
Natural Language Processing: Getting Started 
Natural Language Processing is all about how machines try to understand and interpret human 
language and operate accordingly. But how can Natural Language Processing be used to solve the 
problems around us? Let us take a look. 
Revisiting the AI Project Cycle 
Let us try to understand how we can develop a project in Natural Language processing with the help 
of an example. 
The Scenario 
The world is competitive nowadays. People face 
competition in even the tiniest tasks and are expected to 
give their best at every point in time. When people are 
unable to meet these expectations, they get stressed and 
could even go into depression. We get to hear a lot of cases 
where people are depressed due to reasons like peer 
pressure, studies, family issues, relationships, etc. and they 
eventually get into something that is bad for them as well 
as for others. So, to overcome this, cognitive behavioural 
therapy (CBT) is considered to be one of the best methods 
to address stress as it is easy to implement on people and 
also gives good results. This therapy includes 
Page 4


 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Natural Language Processing 
Introduction 
Till now, we have explored two domains of AI: Data Science and Computer Vision. Both these domains 
differ from each other in terms of the data on which they work. Data Science works around numbers 
and tabular data while Computer Vision is all about visual data like images and videos. The third 
domain, Natural Language Processing (commonly called NLP) takes in the data of Natural Languages 
which humans use in their daily lives and operates on this. 
Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to 
understand and process human languages. AI is a subfield of Linguistics, Computer Science, 
Information Engineering, and Artificial Intelligence concerned with the interactions between 
computers and human (natural) languages, in particular how to program computers to process and 
analyse large amounts of natural language data. 
But how do computers do that? How do they understand what we say in our language? This chapter 
is all about demystifying the Natural Language Processing domain and understanding how it works. 
Before we get deeper into NLP, let us experience it with the help of this AI Game: 
 
Identify the mystery animal: http://bit.ly/iai4yma  
 
 
Go to this link on Google Chrome, launch the experiment and try to identify the Mystery Animal by 
asking the machine 20 Yes or No questions. 
Were you able to guess the animal? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If yes, in how many questions were you able to guess it? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If no, how many times did you try playing this game? 
__________________________________________________________________________________
__________________________________________________________________________________ 
What according to you was the task of the machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Were there any challenges that you faced while playing this game? If yes, list them down. 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach must one follow to win this game? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Natural Language Processing 
Since Artificial Intelligence nowadays is becoming an integral part of our lives, its applications are very 
commonly used by the majority of people in their daily lives. Here are some of the applications of 
Natural Language Processing which are used in the real-life scenario: 
Automatic Summarization: Information overload is a real 
problem when we need to access a specific, important piece 
of information from a huge knowledge base. Automatic 
summarization is relevant not only for summarizing the 
meaning of documents and information, but also to 
understand the emotional meanings within the information, 
such as in collecting data from social media. Automatic 
summarization is especially relevant when used to provide an 
overview of a news item or blog post, while avoiding 
redundancy from multiple sources and maximizing the 
diversity of content obtained. 
Sentiment Analysis: The goal of sentiment 
analysis is to identify sentiment among several 
posts or even in the same post where emotion is 
not always explicitly expressed. Companies use 
Natural Language Processing applications, such as 
sentiment analysis, to identify opinions and 
sentiment online to help them understand what 
customers think about their products and services 
(i.e., “I love the new iPhone” and, a few lines later 
“But sometimes it doesn’t work well” where the 
person is still talking about the iPhone) and overall 
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
indicators of their reputation. Beyond determining simple polarity, sentiment analysis understands 
sentiment in context to help better understand what’s behind an expressed opinion, which can be 
extremely relevant in understanding and driving purchasing decisions. 
 
Text classification: Text classification makes it possible to assign 
predefined categories to a document and organize it to help you 
find the information you need or simplify some activities. For 
example, an application of text categorization is spam filtering in 
email. 
 
 
Virtual Assistants: Nowadays Google Assistant, Cortana, 
Siri, Alexa, etc have become an integral part of our lives. Not 
only can we talk to them but they also have the abilities to 
make our lives easier. By accessing our data, they can help 
us in keeping notes of our tasks, make calls for us, send 
messages and a lot more. With the help of speech 
recognition, these assistants can not only detect our speech 
but can also make sense out of it. According to recent 
researches, a lot more advancements are expected in this 
field in the near future. 
Natural Language Processing: Getting Started 
Natural Language Processing is all about how machines try to understand and interpret human 
language and operate accordingly. But how can Natural Language Processing be used to solve the 
problems around us? Let us take a look. 
Revisiting the AI Project Cycle 
Let us try to understand how we can develop a project in Natural Language processing with the help 
of an example. 
The Scenario 
The world is competitive nowadays. People face 
competition in even the tiniest tasks and are expected to 
give their best at every point in time. When people are 
unable to meet these expectations, they get stressed and 
could even go into depression. We get to hear a lot of cases 
where people are depressed due to reasons like peer 
pressure, studies, family issues, relationships, etc. and they 
eventually get into something that is bad for them as well 
as for others. So, to overcome this, cognitive behavioural 
therapy (CBT) is considered to be one of the best methods 
to address stress as it is easy to implement on people and 
also gives good results. This therapy includes 
 
 
understanding the behaviour and mindset of a person in their normal life. With the help of CBT, 
therapists help people overcome their stress and live a happy life. 
To understand more about the concept of this therapy, visit this link: 
https://en.wikipedia.org/wiki/Cognitive_behavioral_therapy  
Problem Scoping 
CBT is a technique used by most therapists to cure patients out of stress and depression. But it has 
been observed that people do not wish to seek the help of a psychiatrist willingly. They try to avoid 
such interactions as much as possible. Thus, there is a need to bridge the gap between a person who 
needs help and the psychiatrist. Let us look at various factors around this problem through the 4Ws 
problem canvas. 
Who Canvas – Who has the problem? 
Who are the 
stakeholders? 
o People who suffer from stress and are at the onset of depression. 
What do we know 
about them? 
o People who are going through stress are reluctant to consult a psychiatrist. 
 
What Canvas – What is the nature of the problem? 
What is the 
problem? 
o People who need help are reluctant to consult a psychiatrist and hence live 
miserably. 
How do you know 
it is a problem? 
o Studies around mental stress and depression available on various authentic 
sources. 
 
Where Canvas – Where does the problem arise? 
What is the context/situation 
in which the stakeholders 
experience this problem? 
o When they are going through a stressful period of time 
o Due to some unpleasant experiences 
 
Why Canvas – Why do you think it is a problem worth solving? 
What would be of key 
value to the stakeholders? 
o People get a platform where they can talk and vent out their 
feelings anonymously 
o People get a medium that can interact with them and applies 
primitive CBT on them and can suggest help whenever needed 
How would it improve their 
situation? 
o People would be able to vent out their stress  
o They would consider going to a psychiatrist whenever required 
 
Page 5


 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Natural Language Processing 
Introduction 
Till now, we have explored two domains of AI: Data Science and Computer Vision. Both these domains 
differ from each other in terms of the data on which they work. Data Science works around numbers 
and tabular data while Computer Vision is all about visual data like images and videos. The third 
domain, Natural Language Processing (commonly called NLP) takes in the data of Natural Languages 
which humans use in their daily lives and operates on this. 
Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to 
understand and process human languages. AI is a subfield of Linguistics, Computer Science, 
Information Engineering, and Artificial Intelligence concerned with the interactions between 
computers and human (natural) languages, in particular how to program computers to process and 
analyse large amounts of natural language data. 
But how do computers do that? How do they understand what we say in our language? This chapter 
is all about demystifying the Natural Language Processing domain and understanding how it works. 
Before we get deeper into NLP, let us experience it with the help of this AI Game: 
 
Identify the mystery animal: http://bit.ly/iai4yma  
 
 
Go to this link on Google Chrome, launch the experiment and try to identify the Mystery Animal by 
asking the machine 20 Yes or No questions. 
Were you able to guess the animal? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If yes, in how many questions were you able to guess it? 
__________________________________________________________________________________
__________________________________________________________________________________ 
If no, how many times did you try playing this game? 
__________________________________________________________________________________
__________________________________________________________________________________ 
What according to you was the task of the machine? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
  
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
Were there any challenges that you faced while playing this game? If yes, list them down. 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
What approach must one follow to win this game? 
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________
__________________________________________________________________________________ 
Applications of Natural Language Processing 
Since Artificial Intelligence nowadays is becoming an integral part of our lives, its applications are very 
commonly used by the majority of people in their daily lives. Here are some of the applications of 
Natural Language Processing which are used in the real-life scenario: 
Automatic Summarization: Information overload is a real 
problem when we need to access a specific, important piece 
of information from a huge knowledge base. Automatic 
summarization is relevant not only for summarizing the 
meaning of documents and information, but also to 
understand the emotional meanings within the information, 
such as in collecting data from social media. Automatic 
summarization is especially relevant when used to provide an 
overview of a news item or blog post, while avoiding 
redundancy from multiple sources and maximizing the 
diversity of content obtained. 
Sentiment Analysis: The goal of sentiment 
analysis is to identify sentiment among several 
posts or even in the same post where emotion is 
not always explicitly expressed. Companies use 
Natural Language Processing applications, such as 
sentiment analysis, to identify opinions and 
sentiment online to help them understand what 
customers think about their products and services 
(i.e., “I love the new iPhone” and, a few lines later 
“But sometimes it doesn’t work well” where the 
person is still talking about the iPhone) and overall 
 
* Images shown here are the property of individual organisations and are used here for reference purpose only. 
   
indicators of their reputation. Beyond determining simple polarity, sentiment analysis understands 
sentiment in context to help better understand what’s behind an expressed opinion, which can be 
extremely relevant in understanding and driving purchasing decisions. 
 
Text classification: Text classification makes it possible to assign 
predefined categories to a document and organize it to help you 
find the information you need or simplify some activities. For 
example, an application of text categorization is spam filtering in 
email. 
 
 
Virtual Assistants: Nowadays Google Assistant, Cortana, 
Siri, Alexa, etc have become an integral part of our lives. Not 
only can we talk to them but they also have the abilities to 
make our lives easier. By accessing our data, they can help 
us in keeping notes of our tasks, make calls for us, send 
messages and a lot more. With the help of speech 
recognition, these assistants can not only detect our speech 
but can also make sense out of it. According to recent 
researches, a lot more advancements are expected in this 
field in the near future. 
Natural Language Processing: Getting Started 
Natural Language Processing is all about how machines try to understand and interpret human 
language and operate accordingly. But how can Natural Language Processing be used to solve the 
problems around us? Let us take a look. 
Revisiting the AI Project Cycle 
Let us try to understand how we can develop a project in Natural Language processing with the help 
of an example. 
The Scenario 
The world is competitive nowadays. People face 
competition in even the tiniest tasks and are expected to 
give their best at every point in time. When people are 
unable to meet these expectations, they get stressed and 
could even go into depression. We get to hear a lot of cases 
where people are depressed due to reasons like peer 
pressure, studies, family issues, relationships, etc. and they 
eventually get into something that is bad for them as well 
as for others. So, to overcome this, cognitive behavioural 
therapy (CBT) is considered to be one of the best methods 
to address stress as it is easy to implement on people and 
also gives good results. This therapy includes 
 
 
understanding the behaviour and mindset of a person in their normal life. With the help of CBT, 
therapists help people overcome their stress and live a happy life. 
To understand more about the concept of this therapy, visit this link: 
https://en.wikipedia.org/wiki/Cognitive_behavioral_therapy  
Problem Scoping 
CBT is a technique used by most therapists to cure patients out of stress and depression. But it has 
been observed that people do not wish to seek the help of a psychiatrist willingly. They try to avoid 
such interactions as much as possible. Thus, there is a need to bridge the gap between a person who 
needs help and the psychiatrist. Let us look at various factors around this problem through the 4Ws 
problem canvas. 
Who Canvas – Who has the problem? 
Who are the 
stakeholders? 
o People who suffer from stress and are at the onset of depression. 
What do we know 
about them? 
o People who are going through stress are reluctant to consult a psychiatrist. 
 
What Canvas – What is the nature of the problem? 
What is the 
problem? 
o People who need help are reluctant to consult a psychiatrist and hence live 
miserably. 
How do you know 
it is a problem? 
o Studies around mental stress and depression available on various authentic 
sources. 
 
Where Canvas – Where does the problem arise? 
What is the context/situation 
in which the stakeholders 
experience this problem? 
o When they are going through a stressful period of time 
o Due to some unpleasant experiences 
 
Why Canvas – Why do you think it is a problem worth solving? 
What would be of key 
value to the stakeholders? 
o People get a platform where they can talk and vent out their 
feelings anonymously 
o People get a medium that can interact with them and applies 
primitive CBT on them and can suggest help whenever needed 
How would it improve their 
situation? 
o People would be able to vent out their stress  
o They would consider going to a psychiatrist whenever required 
 
 
 
Now that we have gone through all the factors around the problem, the problem statement templates 
go as follows: 
Our People undergoing stress Who? 
Have a problem of Not being able to share their feelings What? 
While They need help in venting out their emotions  Where? 
An ideal solution would 
Provide them a platform to share their thoughts 
anonymously and suggest help whenever required 
Why 
This leads us to the goal of our project which is: 
“To create a chatbot which can interact with people, help them 
to vent out their feelings and take them through primitive CBT.” 
Data Acquisition 
To understand the sentiments of people, we need to collect their conversational data so the machine 
can interpret the words that they use and understand their meaning. Such data can be collected from 
various means: 
1. Surveys     2. Observing the therapist’s sessions  
3. Databases available on the internet   4. Interviews, etc. 
Data Exploration 
Once the textual data has been collected, it needs to be processed and cleaned so that an easier 
version can be sent to the machine. Thus, the text is normalised through various steps and is lowered 
to minimum vocabulary since the machine does not require grammatically correct statements but the 
essence of it. 
Modelling 
Once the text has been normalised, it is then fed to an NLP based AI model. Note that in NLP, modelling 
requires data pre-processing only after which the data is fed to the machine. Depending upon the type 
of chatbot we try to make, there are a lot of AI models available which help us build the foundation of 
our project. 
Evaluation 
The model trained is then evaluated and the accuracy for the same is generated on the basis of the 
relevance of the answers which the machine gives to the user’s responses. To understand the 
efficiency of the model, the suggested answers by the chatbot are compared to the actual answers.