Introduction to Natural language processing

Introduction to Natural language processing

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Updated on Feb 10, 2023 15:34 IST

Natural Language Processing (NLP) refers to the branch of artificial intelligence that gives machines the ability to read, understand, and derive meaning from human language. In this article we will talk about natural language processing and why it is used.We will also discuss its applications and examples.


In the digital world, users can search for information more efficiently and find relevant results. Search engines have become much more sophisticated and can now spot patterns in words and understand what a user is trying to say. The ability to process natural language has made it much easier for humans to communicate with computers and has led to the emergence of powerful search tools and chatbots. Natural language processing (NLP) involves extracting meaning from written text, audio recordings, video clips, or other formats that people use to communicate. Learning to do NLP well will open up countless possibilities in your projects. This article introduces you to the basics of natural language processing and gives examples of how it can be used in real-world scenarios.

Table of contents

What Is Natural Language Processing (NLP)?

Natural language processing uses computers to understand and process human language. To do this, computers need to understand what people are saying, and they also need to be able to create a written or spoken output that people can understand. 

NLP is a vast field, and it can often take time to define it precisely. However, it is often helpful to think of NLP as the ability to understand and process written and spoken text. NLP is beneficial for many types of communication. 

For example, NLP can be used for search and retrieval, for creating content such as emails and documents, for transcription services, for creating and playing speech and audio files, for creating interactive virtual assistants, and for language translation.

Explore: Natural language processing by Microsoft

Also explore: Computer Science Master’s Degree – Natural Language Processing

How Does Natural Language Processing Work?

  1. First, the text is broken into different sentences. This process is called segmentation.
  2. The sentences are broken into words. This process is called tokenization. First, they use a “linguistic model” that maps the structure of the language so that they can understand the different parts of sentences (such as nouns, verbs, adjectives, and adverbs).
  3. Insignificant words that contain little or no unique information are removed from the sentence—for example, prepositions and articles (at, to, a, the). Words are standardized to the root forms. This is called stemming and lemmatization. Example: In the sentence “The man is running,” the algorithm can recognize that the root of the word “running” is “run”.
  4. They use “lexical analysis” to understand the meaning of words. Linguistic models can vary in complexity, but most NLP systems use a “sequence-to-sequence” model. This means that they try to “understand” a sentence as a series of words
  5. Try to understand the “next” words in that series as a “prediction” of what the next word will be. Of course, the “prediction” of one word is often based on the “understanding” of the previous words (such as the word before “prediction”), which makes this a bit of a circular process.

Also check: Become a Natural Language Processing Expert

Check: Hands On Natural Language Processing (NLP) using Python

Why Is Natural Language Processing Important?

  1. It can be used for search and retrieval, for creating content such as emails and documents, for transcription services, creating and playing speech and audio files, for creating interactive virtual assistants, and for language translation. 
  2. These communication channels have many benefits, including improved efficiency, reduced time spent searching, a better understanding of the user’s needs, easier creation of content, better understanding of the user’s desires, improved accessibility, and more.
  3. Given the vast amount of unstructured data generated daily, from medical records to social media, automation is essential to efficiently and thoroughly analyze text and voice data.

Challenges of Natural Language Processing

  1. Natural language processing has become much more widespread over the past few decades, but it is still imperfect. The main challenge is that computers are still trying to understand language through a “predictive” model. Humans can understand language through “transformative” models, which involve “transcribing” the meaning from one part of the language to another. Therefore, computers will never be able to truly “understand” language the same way humans do, but they can achieve good results through “predictive” models.
  2. In NLP, syntactic and semantic analysis is key to understanding the grammatical structure of a text and identifying how words relate to each other in a given context. But converting text into something that machines can process is complicated.
  3. Sometimes one sentence has a different meaning(ambiguity in the text), so the machine is not able to identify the actual meaning of the sentence like 

Example 1 

If there is a sentence the tank is full of water here, the machine needs help understanding which tank we are talking about. Meet the contacts is not understandable for the machine here.

Example 2 

If you have a sentence: The car hit the phone while it was moving, the machine cannot figure out that the car is moving or the pole is.

Natural Language Processing Applications

Search engine

One of the best applications of NLP is in search engines. The ability to process natural language has opened up many new possibilities for search engines and made them much more effective. Structured data, such as terms and descriptions, can be used to create powerful search functions that return accurate results. Natural language processing can also be used to create more advanced search functions, such as sending search queries to servers that crunch numbers to return the most accurate results. 

Web crawling and recommendation engine

Other types of applications include web crawling and recommendation engines. With structured data, these applications can create more accurate results, understand the context of the user’s search and return more relevant results. 

Automate Customer Support Tasks

NLP do Customer Service automation by doing a task like assigning tickets to the appropriate agent and doing chat using chatbots; here are some examples

Text classification models give tags to the incoming support tickets based on criteria like topic, language, and sentiment by using NLP Technology. Like in e-commerce companies, there is the topic classifier that identifies the support ticket category. The category could be missing items, returned item shifting problems, etc. Besides this classifier also detect urgency. This is done by detecting words like immediatelyright now, ASAP, etc. There is MonkeyLearn’s urgency detector which works for this.


Customer service and experience are paramount to any business. It helps businesses improve their products and satisfy their customers. However, manually communicating with each customer to resolve their issues can be a tedious task. In this computer do chat with you on behalf of of a human.This is where chatbots come into play. Chatbots help businesses reach their goal of a seamless customer experience.


Natural Language Processing Examples

Autocomplete function

Companies such as Google and Bing have become incredibly powerful thanks to using NLP. One of the most classic examples of Google’s use of NLP is the autocomplete function. This allows users to type in a few letters of their search query, such as “weather New York” and then see a list of suggestions related to that query. 

Tagging photo

Facebook’s use of NLP is also very classic. For example, if you tag a person in a photo, Facebook will often return search results with the person’s name in the text. This is a perfect example of how NLP can be used to understand the meaning of words.


Want to translate text from English to Hindi but don’t know Hindi? Then Google Translate is for you. It’s not 100% accurate, but it’s an excellent tool for converting text from one language to another. Google Translate and other translation tools also use sequence-to-sequence modeling, a natural language processing technique. This allows algorithms to convert a sequence of words from one language to another. Here is the translation. Language translators used to use statistical machine translation (SMT). That means analyzing millions of documents that have already been translated from one language to another (in this case, from English to Hindi), searching for common patterns and primary vocabulary language.

Natural Language Processing Tools

Natural Language Toolkit(NLTK)

import nltk
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NLTK is a key library that supports tasks such as classification, stemming, tagging, parsing, semantic inference, and tokenization in Python. This is basically the main tool for natural language processing and machine learning. Today, it is an educational foundation for Python developers exploring this field (and machine learning).

This library was developed by Stephen Byrd and Edward Roper at the University of Pennsylvania and played a key role in his groundbreaking NLP research. Many universities worldwide use his NLTK, Python libraries, and other tools in their courses. NLTK can be very slow and needs to meet the needs of fast-paced production use.


import sklearn
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It is a tremendous open library for natural language processing and is most commonly used by data scientists for NLP tasks. It offers a large number of algorithms for building machine-learning models. It has excellent documentation to help data scientists and make learning easier. The main advantage of Sci-Kit Learn is that it has nice and intuitive class methods. Bag-of-words provides many functions for converting text to numeric vectors. It also has some drawbacks. It does not provide neural networks for text preprocessing.


Stanford CoreNLP contains a group of tools for human language innovation. This means making it easy and appropriate to use text semantic analysis tools. With CoreNLP, you can extract various text properties (part-of-speech tags, named entity recognition, etc.) with just a few lines of code.

It provides programming interfaces for several popular programming languages, including Python. The tool integrates various Stanford NLP tools such as sentiment analysis, part-of-speech (POS) tagger, bootstrap pattern learning, parsers, named entity recognition (NER), and cross-reference resolution system, just to name a few.


Pattern enables part-of-speech tagging, sentiment analysis, vector space modeling, SVM, clustering, n-gram search, and WordNet. You can use DOM parsers, web crawlers, and use APIs like Twitter and Facebook. Still, this tool is a web miner and may be inadequate to handle other natural language processing tasks.


import spacy
# Load English tokenizer, tagger, parser and NER
nlp = spacy.load("en_core_web_sm")
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spaCy is a relatively new library designed for production use. This makes it much more accessible than other Python NLP libraries, such as NLTK. spaCy provides the fastest syntax parser currently available on the market. Also, the toolkit is written in Cython, which makes it very fast and efficient.

However, no tool is perfect. Compared to the previously mentioned libraries, spaCy supports the fewest languages ​​(7). However, the growing popularity of machine learning, NLP, and spaCy as leading libraries means tools may take off


<strong>from</strong> textblob <strong>import</strong> TextBlob
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TextBlob is a Python library (2 and 3) for processing text data. It provides a simple API to dive into common NLP (Natural Language Processing) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, and translation. TextBlob is a must-have for any developer starting his NLP journey in Python and wanting to make the most of his first encounter with NLTK.


import polyglot
from polyglot.text import Text,Word
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This lesser-known library is one of our favorites because it offers extensive analysis and impressive language coverage. It’s also very fast, thanks to NumPy. Using polyglot is similar to spaCy. It’s efficient, easy, and perfect for projects that use languages ​​spaCy doesn’t support. The library also stands out because it requires the use of dedicated commands on the command line via a pipeline mechanism.


Natural language processing is one of the essential aspects of computer science. It has opened up countless possibilities in both human-computer interaction and information retrieval. The use of structured data has allowed computers to become much more effective at finding and retrieving relevant information. Natural language has dramatically improved the speed and quality of these interactions. NLP is a fascinating and challenging field that will continue to open new doors in computer science and consumer technology.

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