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Komal . Seminar .NLP

Computer Engineering (Dr. Babasaheb Ambedkar Technological University)

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Seminar

Report on

“ Natural Language Processing ”

Submitted In Fulfillment of the Requirement

Second year of

Bachelor of Technology

In

Artificial Intelligence and Data Science

Hindi Seva Mandal’s (Estd. 1950)

SHRI SANT GADGE BABA

COLLEGE OF ENGINEERING AND TECHNOLOGY
Bhusawal – 425203. Dist. Jalgaon (M. S.)
Affiliated to
Dr. Babasaheb Ambedkar Technological University Lonere, Maharashtra

Submitted By
Ms. KOMAL LALIT PATIL
PRN -2151701995008
2022-2023

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Hindi Seva Mandal’s (Estd. 1950)

SHRI SANT GADGE BABA

COLLEGE OF ENGINEERING AND TECHNOLOGY
Bhusawal – 425203. Dist. Jalgaon (M. S.)
Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra

CERTIFICATE

This is to certify that Ms . Komal Lalit Patil has undergone and

successfully completed her “seminar ” in Natural Language
Processing for the fulfillment of the Second year of Bachelor of
Technology in Artificial Intelligence and Data Science as prescribed by
Dr. Babasaheb Ambedkar Technological University, Lonere during
academic year 2022-2023

Prof. S .M . Shinde Prof. D .G. Agrawal

Guide Head of Department

Prof. R. A. Agrawal Dr. R. B. Barjibhe

Training and Placement Officer Principal

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ACKNOWLEDGEMENT

I feel great pleasure in submitting this seminar report on “Natural Language

Processing”

I would like to express profound gratitude to Prof. S .M . Shinde for her

valuable support ,encouragement ,supervision and useful suggestions throught this report
work. Her moral support and continuous guidance enable mw to completed my work
successfully.

I am thankful to my Head of Department, Prof .D .G. Agrawal for his

continuous motivation and support.

I take this opportunity to thank our Hon. Principal, Prof. Dr. R. B. Barjibhe for
setting high benchmarks and allowing us for seminars .

I am thankful to our Training and Placement Officer, Prof. R. A. Agrawal for

providing us with the best opportunities, making prior arrangements, encouragement and
developing our positive attitude towards Industrial exposure, Internships with quality
placements.

Ms. KOMAL LALIT PATIL

PRN -2151701995008
2022-2023
S. Y. B. Tech.
Artificial intelligence and data science

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ABSTRACT

Natural language processing, or NLP, is a type of artificial

intelligence that deals with analyzing, understanding, and
generating natural human languages so that computers can
process written and spoken human language without using
computer-driven language. Natural language processing,
sometimes also called “computational linguistics,” uses both
semantics and syntax to help computers understand how
humans talk or write and how to derive meaning from what
they say. This field combines the power of artificial
intelligence and computer programming into an
understanding so powerful that programs can even translate
one language into another reasonably accurately. This field
also includes voice recognition, the ability of a computer to
understand what you say well enough to respond
appropriately.

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DECLARATION

I hereby declare that the seminar report entitled , “Natural Language

Processing ” was carried out and written by me under the guidance of
Prof .S .M . Shinde , associate professor , department of artificial
intelligence and data science engineering. Shri .Sant Gadge Baba
college of engineering and technology , Bhusawal .

KOMAL LALIT PATIL

PLACE – BHUSAWAL
DATE -

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INDEX

SR.NO TOPICS PAGE NO.

1 INTRODUCTION 1

2 OBJECTIVES 5

3 BRIEF HISTORY 6

4 NLP APPLICATIONS 11

5 NLP GOALS 14

6 NLP STRUCTURE 15

7 FUTURE SCOPE 18

8 CONCLUTION 19

9 REFEREANCE 20

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INTRODUCTION

Natural language processing (NLP) is the intersection of computer

science, linguistics and machine learning. The field focuses on
communication between computers and humans in natural language
and NLP is all about making computers understand and generate
human language.

Natural language processing studies interactions between humans and

computers to find ways for computers to process written and spoken
words similar to how humans do. The field blends computer science,
linguistics and machine learning.
Natural language processing has heavily benefited from recent
advances in machine learning, especially from deep learning
techniques. The field is divided into the three parts:

 Speech recognition — the translation of spoken language into text.

 Natural language understanding — a computer’s ability to understand
language.
 Natural language generation — the generation of natural language by a
computer

Human language is special for several reasons. It is specifically

constructed to convey the speaker/writer's meaning. It is a complex
system, although little children can learn it pretty quickly.

Another remarkable thing about human language is that it is all about

symbols. According to Chris Manning, a machine learning professor
at Stanford, it is a discrete, symbolic, categorical signaling system.
This means we can convey the same meaning in different ways (i.e.,
speech, gesture, signs, etc.) The encoding by the human brain is a
continuous pattern of activation by which the symbols are
transmitted via continuous signals of sound and vision.

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Understanding human language is considered a difficult task due to

its complexity. For example, there are an infinite number of different
ways to arrange words in a sentence. Also, words can have several
meanings and contextual information is necessary to correctly
interpret sentences. Every language is more or less unique and
ambiguous. Just take a look at the following newspaper headline “The
Pope’s baby steps on gays.” This sentence clearly has two very
different interpretations, which is a pretty good example of the
challenges in natural language processing.

TEXT SEGMENTATION

Text segmentation in natural language processing is the process of

transforming text into meaningful units like words, sentences,
different topics, the underlying intent and more. Mostly, the text is
segmented into its component words, which can be a difficult task,
depending on the language. This is again due to the complexity of
human language. For example, it works relatively well in English to
separate words by spaces, except for words like “icebox” that belong
together but are separated by a space. The problem is that people
sometimes also write it as “ice-box.”

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Relationship Extraction

Relationship extraction takes the named entities of NER and

tries to identify the semantic relationships between them.
This could mean, for example, finding out who is married to
whom, that a person works for a specific company and so on.
This problem can also be transformed into a classification
problem and a machine learning model can be trained for
every relationship type.

Sentiment Analysis

With sentiment analysis we want to determine the attitude (i.e. the

sentiment) of a speaker or writer with respect to a document,
interaction or event. Therefore it is a natural language processing
problem where text needs to be understood in order to predict the
underlying intent. The sentiment is mostly categorized into positive,
negative and neutral categories.

With the use of sentiment analysis, for example, we may want to predict
a customer’s opinion and attitude about a product based on a review
they wrote. Sentiment analysis is widely applied to reviews, surveys,
documents and much more.

If you’re interested in using some of these techniques with Python, take

a look at the Jupyter Notebook about Python’s natural language toolkit
(NLTK) that I created. You can also check out my blog post about
building neural networks with Keras where I train a neural network to
perform sentiment analysis.

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Deep Learning and Natural Language

Processing

Central to deep learning and natural language is “word meaning,”

where a word and especially its meaning are represented as a vector of
real numbers. With these vectors that represent words, we are placing
words in a high-dimensional space. The interesting thing about this is
that the words, which are represented by vectors, will act as a
semantic space. This simply means the words that are similar and have
a similar meaning tend to cluster together in this high-dimensional
vector space. You can see a visual representation of word meaning
below:

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OBJECTIVES

Spellcheck and search are so mainstream, that we often take for

granted, especially at work where Natural Language Processing
provides several productivity benefits.

at work, if you want to know the information about your leaves, you
can save the time of asking questions to your Human Resource
Manager. There is a chatbot based searches in the companies to whom
you can request a question and get answers about any policy of the
company. The integrated search tools in companies make customer
resource calls and accounting up to 10x shorter.

In addition, NLP helps recruiters in sorting job profiles, attract varied

candidates, and select employees that are more qualified. NLP also
helps in spam detection and keeps unwanted emails out of your
mailbox. Gmail and Outlook use NLP to label messages from specific
senders into folders you create.

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BRIEF HISTORY

In the early 1900s, a Swiss linguistics professor named Ferdinand de

Saussure died, and in the process, almost deprived the world of the
concept of “Language as a Science.” From 1906 to 1911, Professor
Saussure offered three courses at the University of Geneva, where he
developed an approach describing languages as “systems.” Within the
language, a sound represents a concept – a concept that shifts
meaning as the context changes.

He argued that meaning is created inside language, in the relations and

differences between its parts. Saussure proposed “meaning” is
created within a language’s relationships and contrasts. A shared
language system makes communication possible. Saussure viewed
society as a system of “shared” social norms that provides conditions
for reasonable, “extended” thinking, resulting in decisions and actions
by individuals. (The same view can be applied to modern computer
languages).

Saussure died in 1913, but two of his colleagues, Albert Sechehaye and
Charles Bally, recognized the importance of his concepts. (Imagine the
two, days after Saussure’s death, in Bally’s office, drinking coffee and
wondering how to keep his discoveries from being lost forever). The
two took the unusual steps of collecting “his notes for a manuscript,”
and his students’ notes from the courses. From these, they wrote
the Cours de Linguistique Générale, published in 1916. The book laid
the foundation for what has come to be called the structuralist
approach, starting with linguistics, and later expanding to other fields,
including computers.

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In 1950, Alan Turing wrote a paper describing a test for a “thinking”

machine. He stated that if a machine could be part of a conversation
through the use of a teleprinter, and it imitated a human so completely
there were no noticeable differences, then the machine could be
considered capable of thinking. Shortly after this, in 1952, the Hodgkin-
Huxley model showed how the brain uses neurons in forming an
electrical network. These events helped inspire the idea of Artificial
Intelligence (AI), Natural Language Processing (NLP), and the evolution
of computers.

Natural Language Processing

Natural Language Processing (NLP) is an aspect of Artificial

Intelligence that helps computers understand, interpret, and utilize
human languages. NLP allows computers to communicate with people,
using a human language. Natural Language Processing also provides
computers with the ability to read text, hear speech, and interpret it.
NLP draws from several disciplines, including computational linguistics
and computer science, as it attempts to close the gap between human
and computer communications.

Generally speaking, NLP breaks down language into shorter, more

basic pieces, called tokens (words, periods, etc.), and attempts to
understand the relationships of the tokens. This process often uses
higher-level NLP features, such as:

 Content Categorization: A linguistic document summary that includes

content alerts, duplication detection, search, and indexing.
 Topic Discovery and Modeling: Captures the themes and meanings of
text collections, and applies advanced analytics to the text.
 Contextual Extraction: Automatically pulls structured data from text-
based sources.
 Sentiment Analysis: Identifies the general mood, or subjective opinions,
stored in large amounts of text. Useful for opinion mining.
 Text-to-Speech and Speech-to-Text Conversion: Transforms voice
commands into text, and vice versa.
 Document Summarization: Automatically creates a synopsis,
condensing large amounts of text.

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NLP Begins and Stops

Noam Chomsky published his book, Syntactic Structures, in 1957. In it,

he revolutionized previous linguistic concepts, concluding that for a
computer to understand a language, the sentence structure would
have to be changed. With this as his goal, Chomsky created a style of
grammar called Phase-Structure Grammar, which methodically
translated natural language sentences into a format that is usable by
computers. (The overall goal was to create a computer capable of
imitating the human brain, in terms of in thinking and communicating,
or AI.)

In 1958, the programming language LISP (Locator/Identifier Separation

Protocol), a computer language still in use today, was released by John
McCarthy. In 1964, ELIZA, a “typewritten” comment and response
process, designed to imitate a psychiatrist using reflection techniques,
was developed. (It did this by rearranging sentences and following
relatively simple grammar rules, but there was no understanding on the
computer’s part.) Also in 1964, the U.S. National Research Council
(NRC) created the Automatic Language Processing Advisory
Committee, or ALPAC, for short. This committee was tasked with
evaluating the progress of Natural Language Processing research.

In 1966, the NRC and ALPAC initiated the first AI and NLP stoppage, by
halting the funding of research on Natural Language Processing and
machine translation. After twelve years of research, and $20 million
dollars, machine translations were still more expensive than manual
human translations, and there were still no computers that came
anywhere near being able to carry on a basic conversation. In 1966,
Artificial Intelligence and Natural Language Processing (NLP) research
was considered a dead end by many (though not all).

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Return of the NLP

It took nearly fourteen years (until 1980) for Natural Language

Processes and Artificial Intelligence research to recover from the
broken expectations created by extreme enthusiasts. In some ways,
the AI stoppage had initiated a new phase of fresh ideas, with earlier
concepts of machine translation being abandoned, and new ideas
promoting new research, including expert systems. The mixing of
linguistics and statistics, which had been popular in early NLP
research, was replaced with a theme of pure statistics. The 1980s
initiated a fundamental reorientation, with simple approximations
replacing deep analysis, and the evaluation process becoming more
rigorous.

Until the 1980s, the majority of NLP systems used complex,

“handwritten” rules. But in the late 1980s, a revolution in NLP came
about. This was the result of both the steady increase of computational
power, and the shift to Machine Learning algorithms. While some of the
early Machine Learning algorithms (decision trees provide a good
example) produced systems similar to the old school handwritten rules,
research has increasingly focused on statistical models. These
statistical models are capable making soft, probabilistic decisions.
Throughout the 1980s, IBM was responsible for the development of
several successful, complicated statistical models.

In the 1990s, the popularity of statistical models for Natural Language

Processes analyses rose dramatically. The pure statistics NLP
methods have become remarkably valuable in keeping pace with the
tremendous flow of online text. N-Grams have become useful,
recognizing and tracking clumps of linguistic data, numerically. In
1997, LSTM recurrent neural net (RNN) models were introduced, and
found their niche in 2007 for voice and text processing. Currently,
neural net models are considered the cutting edge of research and
development in the NLP’s understanding of text and speech
generation.

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After the Year 2000

In 2001, Yoshio Bengio and his team proposed the first neural
“language” model, using a feed-forward neural network. The feed-
forward neural network describes an artificial neural network that does
not use connections to form a cycle. In this type of network, the data
moves only in one direction, from input nodes, through any hidden
nodes, and then on to the output nodes. The feed-forward neural
network has no cycles or loops, and is quite different from the
recurrent neural networks.

In the year 2011, Apple’s Siri became known as one of the world’s first
successful NLP/AI assistants to be used by general consumers. Within
Siri, the Automated Speech Recognition module translates the owner’s
words into digitally interpreted concepts. The Voice-Command system
then matches those concepts to predefined commands, initiating
specific actions. For example, if Siri asks, “Do you want to hear your
balance?” it would understand a “Yes” or “No” response, and act
accordingly.

By using Machine Learning techniques, the owner’s speaking pattern

doesn’t have to match exactly with predefined expressions. The sounds
just have to be reasonably close for an NLP system to translate the
meaning correctly. By using a feedback loop, NLP engines can
significantly improve the accuracy of their translations, and increase
the system’s vocabulary. A well-trained system would understand the
words, “Where can I get help with Big Data?” “Where can I find an
expert in Big Data?,” or “I need help with Big Data,” and provide the
appropriate response.

The combination of a dialog manager with NLP makes it possible to

develop a system capable of holding a conversation, and sounding
human-like, with back-and-forth questions, prompts, and answers. Our
modern AIs, however, are still not able to pass Alan Turing’s test, and
currently do not sound like real human beings. (Not yet, anyway.)

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NLP APPLICATIONS

1. Email filtering
Email is a part of our everyday life. Whether it is related to work or
studies or many other things, we find ourselves plunged into the pile of
emails. We receive all kinds of emails from various sources; some are
work-related or from our dream school or university, while others are
spam or promotional emails. Here Natural Language Processing comes
to work. It identifies and filters incoming emails into “important” or
“spam” and places them into their respective designations.

2. Language translation
There are as many languages in this world as there are cultures, but
not everyone understands all these languages. As our world is now a
global village owing to the dawn of technology, we need to
communicate with other people who speak a language that might be
foreign to us. Natural Language processing helps us by translating the
language with all its sentiments.

3. Smart assistants
In today’s world, every new day brings in a new smart device, making
this world smarter and smarter by the day. And this advancement is not
just limited to machines. We have advanced enough technology to have
smart assistants, such as Siri, Alexa, and Cortana. We can talk to them
like we talk to normal human beings, and they even respond to us in the
same way.

All of this is possible because of Natural Language Processing. It helps

the computer system understand our language by breaking it into parts
of speech, root stem, and other linguistic features. It not only helps
them understand the language but also in processing its meaning and
sentiments and answering back in the same way humans do.

4. Document analysis
Another one of NLP’s applications is document analysis. Companies,
colleges, schools, and other such places are always filled to the brim
with data, which needs to be sorted out properly, maintained, and
searched for. All this could be done using NLP. It not only searches a
keyword but also categorizes it according to the instructions and saves
us from the long and hectic work of searching for a single person’s
information from a pile of files. It is not only limited to this but also helps
its user to inform decision-making on claims and risk management.

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5. Online searches
results even when you do not know the exact keywords you need to
search for the needed information? Well, the answer is obvious. In this
world full of challenges and puzzles, we must constantly find our way
by getting the required information from available sources. One of the
most extensive

information sources is the internet. We type what we want to search

and checkmate! We have got what we wanted. But have you ever
thought about how you get these

It is again Natural Language Processing. It helps search engines

understand what is asked of them by comprehending the literal
meaning of words and the intent behind writing that word, hence giving
us the results, we want.

6. Predictive text
A similar application to online searches is predictive text. It is
something we use whenever we type anything on our smartphones.
Whenever we type a few letters on the screen, the keyboard gives us
suggestions about what that word might be and when we have written a
few words, it starts suggesting what the next word could be. These
predictive texts might be a little off in the beginning.

Still, as time passes, it gets trained according to our texts and starts to
suggest the next word correctly even when we have not written a single
letter of the next word. All this is done using NLP by making our
smartphones intelligent enough to suggest words and learn from our
texting habits.

7. Automatic summarization
With the increasing inventions and innovations, data has also
increased. This increase in data has also expanded the scope of data
processing. Still, manual data processing is time taking and is prone to
error. NLP has a solution for that, too, it can not only summarize the
meaning of information, but it can also understand the emotional
meaning hidden in the information. Thus, making the summarization
process quick and impeccable.

8. Sentiment analysis
The daily conversations, the posted content and comments, book,
restaurant, and product reviews, hence almost all the conversations
and texts are full of emotions. Understanding these emotions is as
important as understanding the word-to-word meaning. We as humans
can interpret emotional sentiments in writings and conversations, but
with the help of natural language processing, computer systems can
also understand the sentiments of a text along with its literal
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9. Chatbots
With the increase in technology, everything has been digitalized, from
studying to shopping, booking tickets, and customer service. Instead of
waiting a long time to get some short and instant answers, the chatbot
replies instantly and accurately. NLP gives these chatbots
conversational capabilities, which help them respond appropriately to
the customer’s needs instead of just bare-bones replies.

Chatbots also help in places where human power is less or is not

available round the clock. Chatbots operating on NLP also have
emotional intelligence, which helps them understand the customer’s
emotional sentiments and respond to them effectively.

10. Social media monitoring

Nowadays, every other person has a social media account where they
share their thoughts, likes, dislikes, experiences, etc., which tells a lot
about the individuals. We do not only find information about individuals
but also about the products and services. The relevant companies can
process this data to get information about their products and services
to improve or amend them. NLP comes into play here.

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NLP GOALS
The goal of natural language processing is to specify a language
comprehension and production theory to such a level of detail that a
person is able to write a computer program which can understand and
produce natural language. The basic goal of NLP is to accomplish
human like language processing. The choice of word “processing” is
very deliberate and should not be replaced with “understanding”. For
although the field of NLP was originally referred to as Natural
Language Understanding (NLU), that goal has not yet been
accomplished. A full NLU system would be able to:

® Paraphrase an input text.

® Translate the text into another language.

® Answer questions about the contents of the text.

® Draw inferences from the text.

While NLP has made serious inroads into accomplishing goals from first
to third, the fact that NLP system can not, of themselves, draw
inferences from text, NLU still remains the goal of NLP. Also there are
some practical applications of NLP. An NLP-based IR system has the
goal of providing more precise, complete information in response to a
user’s real information need. The goal of the NLP system is to represent
the true meaning and intent of the user’s query, which can be expressed
as naturally in everyday language.

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NLP STRUCTURE

NLP tools transform text into something a machine can understand,

then machine learning algorithms are fed training data and expected
outputs (tags) to train machines to make associations between a
particular input and its corresponding output. Machines then use
statistical analysis methods to build their own “knowledge bank” and
discern which features best represent the texts, before making
predictions for unseen data (new texts):

Ultimately, the more data these NLP algorithms are fed, the more
accurate the text analysis models will be.

Sentiment analysis (seen in the above chart) is one of the most popular
NLP tasks, where machine learning models are trained to classify text
by polarity of opinion (positive, negative, neutral, and everywhere in
between).

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FUTURE SCOPE

The future of Natural Language Processing (NLP) is a little bit

unpredictable, but it is clear that it will be a part of our daily
lives in the next few years. NLP is the process of understanding
natural human language. In other words, it is the ability for
machines and computers to understand human language. The
first phrase that comes to mind, when we hear about NLP is
“Siri”, which is a personal assistant for the iPhone. Siri can
understand what you are saying, but it can’t understand what
you mean. The future of NLP is to have machines that can
understand and have a general understanding of human
language. This would allow us to interact with machines in
ways that we do with other humans.

Natural Language Processing is a term that has been around

for decades and has become an everyday part of our lives.
From the moment we wake up, to the moment we go to sleep,
we interact with NLP. Whether we know it or not, Natural
Language Processing is the technology that powers many of the
everyday things we do. It is the backbone of chatbots, Siri,
Alexa, Google and other voice-activated devices. The
development of Natural Language Processing has been a
relatively slow process, but in recent years, it has made
massive strides. In the last couple of years, NLP has become
part of the public consciousness due to its rapid development
and the increasing number of applications. NLP has also been
getting a lot of attention because of its potential to improve the
way we do things. This is why NLP has been a trending topic in
the last few years. 16

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The implications are quite huge — computers will be able to

understand what we say and what we mean by the words we
use. This means that we’ll be able to create machines that can
not only understand what we want, but also predict what we’re
going to want. Machines will be able to read our minds! Well,
not really. But they’ll be able to help us in ways we can’t even
imagine right now.

An area of study that often focuses on the statistical and

mathematical underpinnings of natural language processing
and sometimes includes the more theoretical side of the field,
such as work on natural language semantics. NLP is also seen
as an information interface between humans and computers.
Natural language processing has been the topic of research for
more than 50 years and has many successful real-world
applications today. For example, NLP is frequently used in
information retrieval, text mining, question answering,
machine translation and speech recognition.

It has only been around since the early 1960s, when

researchers first started trying to teach computers how to
understand human languages. As with most new technologies,
the first applications weren’t always perfect. For example, the
first spell-checkers were just dictionaries with words in
alphabetical order. No grammar checking, no sentence
structure, just a list of words. In fact, the first spell-checker was
created in the 1950s by a Harvard student named Ward
Farnsworth. His system would print out a list of words and
their most likely misspellings in a box underneath the text
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However, this was a pretty big improvement over just guessing

111words at random, which was the only other option

available. Since the early days, NLP has grown in leaps and
bounds. In the 1970s, IBM created a software program called
STREPS (Syntactic Transformation, Evaluation, and
Production System). This was a pretty big deal at the time
because it was the first program to be able to take a sentence in
one language and translate it into another language. While the
output wasn’t always perfect, it was a huge step forward.

Natural language processing (NLP) has been around for a

while now, but it’s only recently that it’s been making huge
leaps and bounds in terms of improvements. From search
engines like Google and Bing to chatbots, NLP is everywhere.
Some things that NLP can be used for include: Text-based
learning, search, social media analytics, web search, document
management, content analysis, and data analytics and
visualization. Most people don’t realize just how much NLP is
improving and some of the insane changes that are happening,
but it’s set to completely change the gaming industry, search
engines, and even how we communicate with each other.

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CONCLUSION

NLP supposedly makes the job easier but still demands a

human interference. People and the industry fear NLP would
start a trend of job snatching which is true to a certain sense
but it certainly cannot function the way it does without
human inputs. The will to work and cater to the loopholes or
bugs in a machine is the task of a human who is handling it.
Notwithstanding, the advantages of NLP may anger in the
arena of jobs but right now it is the knight in the shining
armor of the industry.

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REFERANCES

WWW.JAVAPOINT.COM

WWW.W3SCHOOLS.COM

https://www.xenonstack.com/blog/evolution-of-nlp

https://www.quora.com/What-is-one-of-the-major-goals-of-research-
in-natural-language-processing

https://www.neurosemantics.com/nlp-goal-setting-model/

https://www.asksid.ai/ai/future-of-nlp-the-future-scope-of-natural-
language-processing-asksid/

https://www.dataversity.net/a-brief-history-of-natural-language-
processing-nlp/

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