Natural Language Processing: A comprehensive overview

NATURAL LANGUAGE
PROCESSING: A
COMPREHENSIVE OVERVIEW
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Have you ever wondered how robots like Sophia or your home assistant can
sound so much like humans and understand what we say? Natural Language
Processing (NLP) technology enables machines to comprehend and
communicate with us using natural language. Humans naturally convey
 

information through words and text, but computers speak the binary
language of 1s and 0s. This poses a challenge: How can we make machines
understand, emulate, and respond intelligently to human speech? NLP is the
branch of arti몭cial intelligence that tackles this challenge. It combines the
몭elds of linguistics and computer science to develop models that allow
machines to read, understand, and derive meaning from human languages.
It equips computers to break down and extract important details from text
and speech by deciphering language structure and rules.
NLP serves as a bridge, connecting human thoughts and ideas to the digital
world. It unlocks the vast reservoir of unstructured information, transforming
words into valuable knowledge and data into actionable insights. As per
Markets and Markets, with a notable worth of $15.7 billion in 2022, the NLP
market is expected to undergo remarkable growth at a CAGR of 25.7%,
reaching a signi몭cant value of $49.4 billion by 2027. This growth trend
suggests a strong and positive trajectory for the NLP industry in the coming
years.
Now let us take a deep dive into NLP and gain insights into it. What is NLP?
How does it operate? And what are the fundamental components that make
up NLP? This comprehensive article answers all your questions related to
natural language processing.
What is natural language processing?
Key components of natural language processing
Natural Language Understanding (NLU)
Natural Language Generation (NLG)
5 phases of the natural language processing pipeline
How does natural language processing work?
NLP tasks
How to perform text analysis using Python?
Business use cases of NLP

What is natural language processing?
Natural Language Processing (NLP) is a branch of AI that enables computers
to understand and interpret text and spoken words, similar to how humans
do. In today’s digital landscape, organizations accumulate vast amounts of
data from di몭erent sources, such as emails, text messages, social media
posts, videos, and audio recordings. NLP allows organizations to process and
make sense of this data automatically.
With NLP, computers can analyze the intent and sentiment behind human
communication. For example, NLP makes it possible to determine if a
customer’s email is a complaint, a positive review, or a social media post that
expresses happiness or frustration. This language understanding enables
organizations to extract valuable insights and respond to customers in real
time.
The application of Natural Language Processing (NLP) has permeated various
aspects of our daily lives, and its in몭uence continues to expand as language
technology is integrated into diverse 몭elds. From customer service chatbots
in retailing to interpreting and summarizing electronic health records in
medicine, NLP plays an important role in enhancing user experiences and
interactions across industries.
Key components of natural language
processing
Here are the key components of NLP:
Natural Language Understanding (NLU)
NLU is a branch of computer science that focuses on comprehending human
language beyond the surface-level analysis of individual words. It seeks to
understand the meaning, context, intentions, and emotions behind human
communication. By leveraging algorithms and arti몭cial intelligence
techniques, NLU enables computers to analyze and interpret natural
language text, accurately understanding and responding to the sentiments

expressed in written or spoken language.
In NLU, the process of extracting meaning from text involves three key steps.
First, the semantic analysis examines the words used and their context to
determine their meaning. This step considers how words can have di몭erent
interpretations based on their surrounding context. The second, i.e.,
syntactic analysis, focuses on the grammatical structure of sentences,
analyzing word order and combinations to derive meaning. The third,
discourse analysis, explores the relationships between sentences, identifying
the main subject and understanding how each sentence contributes to the
text’s overall meaning. NLU systems leverage these steps to analyze and
comprehend natural language, enabling them to extract nuanced meanings
from text data.
The NLU system is trained on extensive datasets encompassing diverse
linguistic patterns and contextual variations. These algorithms utilize
information and contextual knowledge to facilitate a more human-like
understanding of language.
Natural Language Generation (NLG)
NLG involves the process of generating text from computer data, serving as a
translator that converts machine representations into natural language. It
functions as the counterpart to NLU, where instead of interpreting language,
NLG focuses on producing coherent and meaningful textual output. The NLG
system uses collected data and user input to generate conclusions or text.
The stages in NLG include content determination and deciding which
information to be included, while document structuring focuses on
organizing the conveyed information. Aggregation merges similar sentences,
and lexical choice selects appropriate words. Expression generation creates
expressions for identi몭cation, and realization ensures grammatical
correctness. These stages collectively contribute to generating coherent and
meaningful text in NLG systems, allowing for the production of natural

language representations from computer data.
These three basic techniques are used for evaluating NLG systems:
Task-based evaluation involves assessing the system’s performance in
helping humans accomplish speci몭c tasks, such as evaluating summaries of
medical data by giving them to doctors and measuring their impact on
decision-making.
Human ratings involve individuals’ subjective assessments of the generated
text’s quality and usefulness.
Metrics comparison entails comparing the generated texts to professionally
written texts, using objective measures to evaluate the system’s output
against established standards. These evaluation techniques provide valuable
insights into the e몭ectiveness and performance of NLG systems, aiding in
their re몭nement and improvement.
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5 phases of the natural language
processing pipeline
The 5 phases of the NLP pipeline are:
Lexical analysis
Lexical analysis is a crucial phase in NLP that focuses on understanding
words’ meanings, relationships, and contexts. It is the initial step in an NLP
pipeline, where the input program is converted into tokens in a speci몭c

order.
Tokens refer to sequences of characters that are treated as a single unit
according to the grammar of the language being analyzed.
Lexical analysis 몭nds applications in various scenarios. For instance, it plays a
vital role in the compilation process of programming languages. In this
context, it takes the input code, breaks it into tokens, and eliminates white
spaces and comments irrelevant to the programming language. Following
tokenization, the analyzer extracts the meaning of the code by identifying
keywords, operations, and variables represented by the tokens.
In the case of chatbots, lexical analysis aids in understanding user input by
looking up tokens in a database to determine the intention behind the words
and their relation to the entire sentence. This form of analysis may involve
considering multiple words together, also known as n-grams, to analyze the
sentence comprehensively.
Parsing
The term “parsing” originates from the Latin word “pars,” meaning “part.” It
refers to the process of breaking down a given sentence into its grammatical
constituents. The objective is to extract the exact meaning or dictionary
meaning from the text. Syntax analysis ensures the text adheres to formal
grammar rules and checks for meaningfulness. For example, a semantic
analyzer would reject a sentence like “hot ice cream” because it lacks
meaningful syntax.
A parser is a software component used to perform parsing tasks. It takes
input data (text) and provides a structural representation of the input by
verifying its correct syntax according to formal grammar. The parser typically
constructs a data structure, such as a parse tree or abstract syntax tree, to
represent the input hierarchically.
The main responsibilities of a parser include reporting syntax errors,

recovering from common errors to allow continued processing of the
program, creating a parse tree, building a symbol table, and generating
intermediate representations.
Semantic analysis
Semantic analysis is the process of comprehending natural language, like
human communication. Its primary goal is to extract the meaning from a
given text by considering the context and nuances. By focusing on the literal
interpretation of words, phrases, and sentences, semantics aims to uncover
the dictionary or actual meaning within the text. This analysis begins by
examining each word, identifying its role within the content, and assessing its
logical and grammatical functions. Moreover, it considers the surrounding
context or corpus to understand the intended meaning better and
disambiguate words with multiple interpretations. Various techniques are
employed to achieve e몭ective semantic analysis:
Co-reference resolution is a technique used to determine the references of
entities in a text, considering not only pronouns but also word phrases like
“this,” “that,” or “it.” By analyzing the context, it identi몭es which phrases refer
to the same entity, aiding in the comprehension of the text.
Semantic role labeling involves identifying the roles of words or phrases in
relation to the main verb of a sentence. It helps in understanding the
semantic relationships and roles played by di몭erent elements in conveying
the meaning of a sentence. This process aids in capturing the underlying
structure and meaning of language.
Word Sense Disambiguation (WSD) is the process of determining the correct
meaning of a word in a given context. It addresses the challenge of resolving
ambiguity by analyzing the surrounding words and context to identify the
most appropriate meaning for a particular word. For example, in the
sentence “I need to deposit money at the bank,” WSD would recognize “bank”
as a 몭nancial institution. While in another example, like “I sat by the bank and

enjoyed the view,” WSD would understand “bank” as the edge of a river
considering the context of sitting and enjoying the view. By disambiguating
words in this manner, WSD improves the accuracy of NLU and facilitates
more precise language processing.
Named Entity Recognition (NER) is a method that identi몭es and categorizes
named entities like persons, locations, and organizations in text. For
example, in the sentence “Manchester United defeated Newcastle United at
Old Tra몭ord,” NER would recognize “Manchester United” and “Newcastle
United” as organizations and “Old Tra몭ord” as a location. NER is used in
various applications such as text classi몭cation, topic modeling, and trend
detection.
Discourse integration
The structure of discourse, or how sentences and clauses are organized, is
determined by the segmentation applied. Discourse relations are key in
establishing connections between these sentences or clauses, ensuring they
몭ow coherently. The meaning of an individual sentence is not isolated but
can be in몭uenced by the context provided by preceding sentences. Similarly,
it can also have an impact on the meaning of the sentences that follow.
Discourse integration is highly important in various NLP tasks, including
information retrieval, text summarization, and information extraction, where
understanding the relationships between sentences is crucial for e몭ective
analysis and interpretation.
Pragmatic analysis
The pragmatic analysis is a linguistic approach that focuses on understanding
a text’s intended meaning by considering the contextual factors surrounding
it. It goes beyond the literal interpretation of words and phrases and
considers the speaker’s intentions, implied meaning, and the social and
cultural context in which the communication occurs.
The key aspect of pragmatic analysis is addressing ambiguity. Natural

language is inherently ambiguous, with words and phrases often having
multiple possible interpretations. Pragmatic analysis helps disambiguate
such instances by considering contextual cues, such as the speaker’s tone,
gestures, and prior knowledge, to determine the intended meaning.
Pragmatic analysis enables the accurate extraction of meaning from text by
considering contextual cues, allowing systems to interpret user queries,
understand 몭gurative language, and recognize implied information. By
considering pragmatic factors, such as the speaker’s goals, presuppositions,
and conversational implicatures, pragmatic analysis enables a deeper
understanding of the underlying message conveyed in a text. It helps bridge
the gap between the explicit information present in the text and the implicit
or intended meaning behind it.
How does natural language processing
work?

work?
NLP models function by establishing connections between the fundamental
elements of language, such as letters, words, and sentences, present in a
given text dataset. To accomplish this, the NLP architecture employs diverse
data pre-processing, feature extraction, and modeling techniques. These
processes include:
Data preprocessing
Data preprocessing is essential in preparing text data for NLP models to
enhance their performance and enable e몭ective understanding. It involves
transforming words and characters into a format the model can readily
comprehend. Data-centric AI emphasizes the signi몭cance of data
preprocessing and considers it a vital component of the overall process. By
prioritizing data preprocessing, AI practitioners aim to optimize the quality
and structure of the input data to maximize the model’s capabilities and
improve its overall performance on speci몭c tasks. Various techniques are
used to preprocess data, which include:
Sentence segmentation: It is the process of breaking a big chunk of text into
smaller, meaningful sentences. In languages like English, we usually use a
period to indicate the end of a sentence. However, it can get tricky because
periods are also used in abbreviations, where they are part of the word. In
some languages, like ancient Chinese, there aren’t clear indicators to mark
the end of a sentence. So, sentence segmentation helps us separate a long
text into meaningful sentences for analysis and understanding.
Tokenization: Tokenization is the process of dividing text into separate words
or word parts. For example, the sentence “I love eating ice cream” would be
tokenized into [“I,” “love,” “eating,” “ice,” “cream”]. This tokenized
representation allows language models to process the text more e몭ciently.
Additionally, by instructing the model to ignore unimportant tokens, such as
common words like “the” or “a,” we can further enhance e몭ciency during

language processing.
Stemming and lemmatization: Stemming is an informal process that applies
heuristic rules to convert words into their base forms. It aims to remove
su몭xes and pre몭xes to obtain the root form of a word. For example,
“university,” “universities,” and “university’s” would all be stemmed to
“univers.” However, stemming may have limitations, such as mapping
unrelated words like “universe” to the same stem.
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Lemmatization is a linguistic process that aims to 몭nd a word’s base form or
root by analyzing its morphology using a vocabulary or dictionary. In
languages like English, words can appear in di몭erent forms based on tense,
number, or other grammatical features. For example, the word “pony” can
appear as “ponies” in its plural form. It considers factors like part of speech
and context to determine the root form accurately. Lemmatization ensures
that the resulting form is a valid word. Libraries like spaCy and NLTK
implement stemming and lemmatization algorithms for NLP tasks.
Stop word removal: In NLP, it’s important to consider the signi몭cance of each
word in a sentence. English contains many 몭ller words like “and,” “the,” and
“a” that occur frequently but don’t carry much meaningful information. These
words can introduce noise when performing statistical analysis on text. To
address this, some NLP pipelines identify these words as stop words,
suggesting they should be 몭ltered out before analysis. Stop words are
commonly determined using a prede몭ned list, although no universal list is
suitable for all applications. The choice of stop words depends on the speci몭c

suitable for all applications. The choice of stop words depends on the speci몭c
context and application.
For instance, if you are building a search engine for rock bands, it would be
unwise to ignore the word “The.” This is because the word “The” appears in
many band names, and there is even a famous rock band from the 1980s
called “The The.” Thus, considering the context is crucial in determining which
words to treat as stop words and which to retain for meaningful analysis.
Feature extraction
Feature extraction refers to the process of converting textual data into
numerical representations. Once the text data is cleaned and normalized, it
needs to be transformed into features that can be understood and
processed by a machine-learning model. Since computers work with
numbers more e몭ciently, we represent individual words or text elements
using numerical values. This numerical representation allows the machine to
process and analyze the data e몭ectively. Feature extraction plays a crucial
role in NLP tasks as it converts text-based information into a format that can
be used for modeling and further analysis. There are various ways in which
this can be done:
Bag-of-words: This approach in NLP counts how many times each word or
group of words appears in a document. It then creates a numerical
representation based on these counts. For example, if we have the sentence
“The cat sat on the mat,” the bag-of-words model would represent it as [1, 1,
1, 1, 1], indicating that each word appears once in the sentence. This helps
convert the text into numbers that can be easily processed by computers,
making it useful for tasks like analyzing document content or training
machine learning models.
Term Frequency-Inverse Document Frequency (TF-IDF): It is a method that
assigns weights to words based on their importance in a document and
across a corpus. It considers two factors: term frequency and inverse
document frequency.

document frequency.
Term frequency measures how important a word is within a document. It
calculates the ratio of the number of times a word appears in a document to
the total number of words in that document.
The inverse document frequency evaluates how important a word is in the
entire corpus. It calculates the logarithm of the ratio between the total
number of documents in the corpus and the number of documents that
contain the word. Words that occur frequently within a document will have a
high TF score. However, common words like “a” and “the” may have high TF
scores even though they are not particularly meaningful. To address this, IDF
gives higher weights to words that are rare in the corpus and lower weights
to common words.
Word2vec: It is a popular method that uses a neural network to generate
high-dimensional word embeddings from raw text. It o몭ers two variations:
Skip-gram and Continuous Bag-of-Words (CBOW). Skip-gram predicts
surrounding words given a target word, while CBOW predicts the target word
from its surrounding words. By training the models on large text corpora and
discarding the 몭nal layer, Word2Vec generates word embeddings that
capture contextual information. Words with similar contexts will have similar
embeddings. These embeddings serve as inputs for various NLP tasks,
enabling algorithms to understand and analyze word meanings and
relationships within a given text.
Global vectors for word representation (GLoVe): It is another method for
learning word embeddings, similar to Word2Vec. However, GLoVe takes a
di몭erent approach using matrix factorization techniques instead of neural
networks. It creates a matrix representing how often words co-occur in a
large text dataset. By analyzing this matrix, GLoVe learns the relationships
between words based on their co-occurrence patterns. These relationships
capture the semantic and syntactic similarities between words. GLoVe
embeddings are useful for understanding word meanings and can be applied

to various language-related tasks.
Modeling
In natural language processing, modeling refers to the process of creating
computational models that can understand and generate human language.
NLP modeling involves designing algorithms, architectures, and techniques
to process and analyze natural language data.
Modeling is the process of building computational models that can
understand and generate human language. These models are designed to
analyze and interpret text data, enabling computers to perform various
language-related tasks.
Several models are used in NLP, but the most popular and e몭ective
approach is based on deep learning. Here are two common types of NLP
models:
Language models: Language models are trained to predict the probability of
a sequence of words in a sentence. They learn the statistical patterns and
relationships in text data, which enables them to generate coherent and
contextually appropriate sentences. Language models can be used for tasks
such as machine translation, text summarization, and speech recognition.
Sequence models: Sequence models are designed to understand the
sequential nature of language. They consider the dependencies between
words and can capture the context and meaning of a sentence. Sequence
models include RNNs and transformer models like the transformer
architecture, which have gained signi몭cant popularity.
These models are trained on large amounts of text data, such as books,
articles, and internet text, to learn the underlying patterns and structures of
language. The training process involves feeding the model with input data
and adjusting its internal parameters to minimize the di몭erence between the
predicted output and the desired output.
NLP tasks

NLP tasks
The intricacies of human language present signi몭cant challenges in
developing software that accurately interprets the intended meaning of text
or voice data. Homonyms, homophones, sarcasm, idioms, metaphors,
grammar exceptions, and variations in sentence structure are just a few of
the complexities that programmers must address in natural language-driven
applications.
Multiple NLP tasks help computers e몭ectively understand and process
human text and voice data. These tasks include:
Speech recognition (speech-to-text): It involves the reliable conversion of
voice data into text data. It is crucial for applications that utilize voice
commands or provide spoken responses. The complexity of speech
recognition arises from the inherent challenges of human speech patterns,
including fast-paced speech, word slurring, diverse emphasis and intonation,
di몭erent accents, and the presence of grammatical errors. Overcoming these
challenges is essential to achieve accurate and e몭ective speech recognition
systems.
Part of speech tagging (grammatical tagging): It is the process of assigning
the appropriate part of speech to a word or piece of text based on its usage
and context. This task involves determining whether a word functions as a
noun, verb, adjective, adverb, or other grammatical categories. For example,
in the sentence “I can make a paper plane,” part of speech tagging identi몭es
“make” as a verb. The sentence “What make of car do you own?” identi몭es
“make” as a noun, indicating that it refers to the type or brand of the car.
Word sense disambiguation: It is the task of choosing the correct meaning of
a word that has multiple possible interpretations based on the context in
which it appears. Through semantic analysis, this process aims to determine
the most appropriate sense of the word in a given context. For instance,
word sense disambiguation helps di몭erentiate between the meanings of the
verb “make” in phrases like “make the grade” (achieve a certain level of

verb “make” in phrases like “make the grade” (achieve a certain level of
success) and “make a bet” (place a wager). By analyzing the surrounding
words and context, word sense disambiguation enables accurate
interpretation and understanding of the intended meaning of ambiguous
words.
Named entity recognition: It is a task that involves identifying and classifying
speci몭c words or phrases in text as named entities or useful entities. NER
identi몭es entities such as names of people, locations, organizations, dates,
and other prede몭ned categories. For example, NER would identify ‘Kentucky’
as a location entity and ‘Fred’ as a person’s name, extracting meaningful
information from text by recognizing and categorizing these named entities.
Co-reference resolution: It is the process of determining whether two or
more words in a text refer to the same entity. This task commonly involves
resolving pronouns to their antecedents, such as determining that ‘she’
refers to ‘Mary.’ However, co-reference resolution can extend beyond
pronouns and include identifying metaphorical or idiomatic references in the
text. For example, it can recognize that in a particular context, the word ‘bear’
does not refer to the animal but instead represents a large hairy person. Co-
reference resolution plays a vital role in understanding the relationships
between di몭erent elements in a text and ensuring accurate comprehension
of the intended meaning.
Sentiment analysis: It is the process of extracting subjective qualities and
determining the sentiment expressed in text. It aims to identify and
understand attitudes, emotions, opinions, sarcasm, confusion, suspicion, and
other subjective written content aspects. By analyzing the language used,
sentiment analysis can categorize text into positive, negative, or neutral
sentiments, providing valuable insights into the overall sentiment conveyed.
This analysis is commonly used in social media monitoring, customer
feedback analysis, market research, and other applications where
understanding sentiment is crucial for decision-making and understanding

public opinion.
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How to perform text analysis using
Python?
Here, the Python library NLTK (Natural Language Toolkit) will be used for text
analysis in English. The NLTK is a group of Python packages created
speci몭cally for locating and tagging components of speech present in texts
written in natural languages.
Step-1: Install NLTK
We may install NLTK in our Python environment by using the command
below:
pip install nltk
If Anaconda is employed, the following command can create a Conda
package for NLTK.
conda install c anaconda nltk
Step-2: Download NLTK data
Downloading NLTK’s prede몭ned text repositories is necessary for easy use
after installation to make it usable. But 몭rst, just like with any other Python

package, we must import NLTK. We may import NLTK by using the command
below.
import nltk
Use the command below to start downloading NLTK data.
nltk.download()
It will take some time to install all available packages of NLTK.
Step-3: Download other necessary packages
Two other essential Python packages for text analysis and natural language
processing (NLP) tasks are gensim and pattern. These packages can be easily
installed using the following commands:
Gensim
Gensim is a powerful library for semantic modeling that can be applied in
various situations. We may install it using the command:
pip install gensim
Pattern
Gensim package functionality can be improved with patterns. The command
below facilitates installing the pattern.
pip install pattern
Step-4: Tokenization

Tokenization is the process of splitting a text into smaller components known
as tokens. Tokens can be letters, numbers, or commas. Another name for it
is word segmentation.
A variety of NLTK packages supports tokenization. Depending on our needs,
we can utilize these packages. Here are the packages and the information on
how to install them:
Sent_tokenize package
To import the package that can be used to divide the input text into
sentences, you can use the following command:
from nltk.tokenize import sent_tokenize
The sent_tokenize function from the nltk.tokenize module allows you to split
a given text into sentences based on language-speci몭c rules and heuristics.
By importing this package, you can leverage its functionality to perform
sentence tokenization, which is a crucial step in many natural language
processing tasks.
Word_tokenize package
To import the package that can be used to divide the input text into words,
you can use the following command:
from nltk.tokenize import word_tokenize
WordPunctTokenizer package
To import the package that can be used to divide the input text into words
and punctuation marks, you can use the following command:
from nltk.tokenize import WordPuncttokenizer

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Step-5: Stemming
Language has many nuances because of grammatical considerations.
Variations in the sense that words can take on several forms in both English
and other languages. As an illustration, consider the words democracy,
democratic, and democratization. It is crucial for machines to comprehend
that various terms, like the ones above, have the same basic shape when
working on machine learning projects. As a result, extracting the word’s basic
forms is highly helpful when analyzing the text.
A heuristic technique known as stemming involves cutting o몭 the ends of
words to reveal their fundamental forms.
The following list includes the several stemming packages o몭ered by the
NLTK module:
Porter stemmer package
This package implements Porter’s stemming algorithm. It can be imported
using the following command:
from nltk.stem.porter import PorterStemmer
For example, when the word ‘writing’ is given as input to this stemmer, the
output will be ‘write.’
Lancaster stemmer package

This package implements Lancaster’s stemming algorithm. It can be imported
using the following command:
from nltk.stem.lancaster import LancasterStemmer
For example, when the word ‘writing’ is given as input to this stemmer, the
output will be ‘writ.’
Snowball stemmer package
To import the SnowballStemmer package, which uses Snowball’s algorithm
for stemming, you can use the following command:
from nltk.stem.snowball import SnowballStemmer
This package allows you to extract the base form of words by applying
Snowball’s stemming algorithm. For example, when you provide the word
‘writing’ as input to this stemmer, the output will be ‘write.’
Step-6: Lemmatization
This package is used to extract the base form of words by removing
in몭ectional endings. It utilizes vocabulary and morphological analysis to
determine the lemma of a word. You can import the WordNetLemmatizer
package using the following command:
from nltk.stem import WordNetLemmatizer
Step-7: Counting POS Tags–Chunking
With the help of chunking, it is possible to identify brief phrases and parts of
speech (POS). It is a crucial step in the processing of natural language. As we
know, tokenization is the method used to produce tokens, while chunking is

the procedure used to label those tokens. In other words, we might claim
that the chunking procedure helps us to obtain the sentence’s structure.
For example, we will use the NLTK Python module to build noun-phrase
chunking, a type of chunking that looks for noun-phrase chunks in the
sentence.
To perform noun-phrase chunking using the NLTK Python module, you can
follow these steps:
Chunk grammar de몭nition: De몭ne the grammar rules for chunking,
specifying patterns to identify noun phrases. For example, you can de몭ne
rules to match determiners, adjectives, and nouns in a sequence.
Chunk parser creation: Create a chunk parser object using the de몭ned
grammar. This parser will apply the grammar rules to the input text and
generate the output.
The output parse: The input text uses the chunk parser to obtain the output
in a tree format. The resulting tree will show the identi몭ed noun phrases and
their structure within the sentence.
By following these steps, you can e몭ectively perform noun-phrase chunking
using the NLTK Python module. The output in tree format allows you to
visualize the structure of noun phrases within the sentence, enabling further
analysis and processing of the text.
Step-8: Running the NLP script
Start by importing the NLTK package −
import nltk
Now, de몭ne the sentence.
Here,

DT is the determinant
VBP is the verb
JJ is the adjective
IN is the preposition
NN is the noun
sentence = [("a", "DT"),("clever","JJ"),("fox","NN"),("was","VBP"),
("jumping","VBP"),("over","IN"),("the","DT"),("wall","NN")]
Next, the grammar should be given in the form of regular expression.
grammar = "NP:{?*}"
Now, we need to de몭ne a parser for parsing the grammar.
parser_chunking = nltk.RegexpParser(grammar)
Now, the parser will parse the sentence as follows −
parser_chunking.parse(sentence)
Next, the output will be in the variable as follows:-
Output = parser_chunking.parse(sentence)
Now, the following code will help you draw your output in the form of a tree.
output.draw()

Business use cases of NLP
Natural language processing has numerous applications in the business
domain. Here are some speci몭c use cases where NLP can be bene몭cial:
Search engine optimization: NLP can help optimize content for online
searches by analyzing searches and understanding how search engines rank
results. By leveraging NLP techniques e몭ectively, businesses can improve
their online visibility and rank higher in search engine results.
Analyzing and organizing large document collections: NLP techniques like
document clustering and topic modeling aid in understanding and organizing
large document collections. This is particularly useful for tasks like legal
discovery, analyzing corporate reports, scienti몭c documents, and news
articles.
Social media analytics: NLP enables scale analysis of customer reviews and
social media comments. Sentiment analysis, in particular, helps identify
positive and negative sentiments in real-time, providing valuable insights for
customer satisfaction, reputation management, and revenue generation.
Market insights: By analyzing customer language, NLP helps businesses gain
insights into customer preferences and improve communication strategies.
Aspect-oriented sentiment analysis helps understand sentiments associated
with speci몭c aspects or products, guiding product design and marketing

with speci몭c aspects or products, guiding product design and marketing
e몭orts.
Moderating content: NLP can assist in content moderation by analyzing the
language, tone, and intent of user or customer comments. This enables
businesses to maintain quality, civility, and a positive online environment.
These applications showcase how NLP can bene몭t businesses signi몭cantly,
ranging from automation and e몭ciency improvements to enhanced
customer understanding and informed decision-making.
Endnote
Natural language processing has emerged as a signi몭cant 몭eld with diverse
applications. It enables machines to understand and process human
language through various components and phases. Tasks like tokenization,
part-of-speech tagging, named entity recognition, and sentiment analysis
contribute to NLP’s e몭ectiveness. NLP has reshaped industries and enhanced
customer experiences with practical use cases like virtual assistants, machine
translation, and text summarization. As NLP continues to advance, with
ongoing research in areas like deep learning and language modeling, we can
anticipate even greater strides in language understanding and
communication. By embracing NLP, we unlock the potential for machines to
e몭ectively interpret, interact, and communicate in human language, paving
the way for exciting advancements in the future.
Want to level up your internal work몭ow and custom-facing systems with NLP-
powered solutions? Connect with LeewayHertz for all your consultancy and
development needs!
Author’s Bio

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of
building over 100+ platforms for startups and enterprises allows Akash to
rapidly architect and design solutions that are scalable and beautiful.
Akash’s ability to build enterprise-grade technology solutions has attracted
over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology
enthusiast, and an investor in AI and IoT startups.
Write to Akash
Start a conversation by filling the form
Once you let us know your requirement, our technical expert will schedule a
call and discuss your idea in detail post sign of an NDA.
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