What is Natural Language Processing? A Comprehensive Guide on NLP

Do you know how computers naturally communicate with humans? 

Well, you know that the computer only understands one and zero in the form of binary. That's true but thanks to AI, computers can understand and respond to human language. 

This is where Natural Language Processing(NLP) comes into play. You have seen applications like chatbots that respond to our queries or translators that translate instantly. NLP is making it all easy.

But how is this possible? 

Don't worry, in this comprehensive blog you will learn everything about what is Natural Language Processing.

So let's begin.

What is Natural Language Processing?

Natural Language Processing refers to a language that is processed naturally. Where Computers can understand what we mean when we speak, enabling them to converse with us, translate languages, analyze feelings in text messages, and even recognize speech. It's like teaching computer bilingualism and emotional intelligence - making our interactions with technology more natural and human-like. 

You may have already interacted with NLP without realizing it. It has existed for more than 50 years and evolved from computer science and linguistics. 

Key Components of NLP

Here are the basic components of NLP: 

Understands Text

NLP makes computers understand written text and allows them to extract meaning from files, emails, blogs, and more.

Recognize Speech

NLP makes computers recognize and transcribe spoken language for different applications and audio devices.

Language Generation

NLP eases the generation of human-like language, allowing computers to produce coherent text and even engage in conversations.

How Do They Understand Your Natural Language?

Well, the language you speak naturally is unstructured, like "Eat bread and butter during breakfast."

You may have understood the meaning but the computer won’t understand it.

To make a computer understand it should be in a structured format, like this: 

Structured format

<breakfast>

<eat> bread</> 

<eat> butter</>

</>

Now the computer can understand what you are trying to say. The job of natural language processing is to translate between these two things. So, NLP sits right in the middle here translating between unstructured and structured data.

The process of translating this unstructured language into structured language is called Natural Language Understanding (NLU), and the process of translating from structured to unstructured language is called Natural Language Generation(NLG).

How Does NLP Work?

As a human, you can differentiate the word “leave” from a tree leaves and a person who is leaving. But how do computers differentiate it?

Well, you can differentiate because you understand grammar. In NLP computers also follow some fundamentals and techniques to preprocess text to understand it like a human language.

So let’s learn those NLP techniques.

1. Tokenization

At first, the computer will break down an unstructured sentence into individual chunks called tokens. For example "I love Berries" is a sentence but after applying tokenization it looks like this “I”, “love”, “Berries”.

Tokenization can be split into two categories. Sentence tokenization and word tokenization. 

Sentence tokenization is separating a paragraph into distinct sentences and word tokenization is separating a sentence into distinct words. This allows the computer to learn the potential meanings and purpose of each unique word.

2. Stop Word Removal

Removes common words from the texts, only the unique words that add valuable information to the sentence remain. Such as prepositions like “at, to” and articles like  “a, an, the.”

3. Stemming and Lemmatization

Once the common words are removed it's time for Stemming. It is the process

of reducing a word from its root form, or stem. By removing its base prefixes and suffixes such as “es, s, ing, and ed.”

For example, the word “Eating“ would be cut down into its root form “Eat.” Stemming is a powerful technique, but sometimes, it cuts unnecessary parts of a root and changes its meaning from the original word.

But don’t worry Lemmatization solves this problem. Instead of cutting off beginnings and endings, Lemmatization reduces a token to its root form and learns its meaning through a dictionary definition. This helps them to identify the word's same core meaning although they appear in different sentences. 

Let’s take a look at what this means. So, if I have the words “Running”, “Ran” and “Runs” which are all formed by the word “Run”. So, Run is the lemma of these words.

4. Parts-of-Speech Tagging

After Lemmatization the next step is parts-of-speech tagging, which distinguishes parts of speech and checks the syntax. Where each tokens are marked based on parts of speech. For example, "Tree leaves are green" and "I leave my home". Both have one common word leave, Where the word ‘leaves’ for the tree is a noun. And for home, the word ‘leave’ is a verb.

5. Text Classification

Text Classification is an important technique in NLP, where texts are classified into predefined categories. It automatically analyzes the patterns within the text and predicts which texts belong to which category. There are different types of text classification like Sentiment Analysis, Topic Modeling, Spam detection, and Keyword extraction.

6. Name Entity Recognition

The last step before applying the algorithms is Name Entity Recognition. Here, the sentence categorizes specific words based on organization, person’s name, location, monetary value, etc. So that it can identify and verify this name as the name of anything or an organization.

For example, I ate an apple at Apple Inc. If you look at the words, the name of the apple is a fruit and organization.

Well after preprocessing, it is time for the machine to understand the language so it needs to build NLP algorithms and train it to perform specific tasks. 

There are many NLP algorithms but in general, only two algorithms are used mostly.

7. Rule-based System

This was the earliest system to create NLP algorithms. In a rule-based system, an expert in linguistics or a programmer defines some grammatical rules, which is later followed by the machines to process the natural language. The algorithm is suitable for problems with simple logic but if any complex problem arises this rule won’t be able to solve it.

8. Machine Learning Algorithm

Machine Learning is a dynamic algorithm based on statistical methods. It can easily solve complex tasks. There is no predefined rule assigned in machine learning; it learns everything from data using algorithms to identify patterns and make decisions. The more data it is fed, the more it can process its task.

Why is NLP Important?

NLP plays an important role in different fields of business. There is so much unstructured text data that is stored as human language in databases, that businesses can’t effectively analyze it. For this problem, only NLP can process it efficiently.  

Let’s find out some reasons for its importance

Where is NLP Used? (Used cases)

Natural Language Processing is a very vast field and its application is used in different industry sectors. 

Some commonly used cases of NLP are:

1. Generative AI

Generative AI in NLP refers to AI models that can create human-like text or responses. It helps in tasks like AI website generation, text generation, image generators, and language translation, improving user interactions and automating content creation.

For example, Dorik AI is the best AI website builder that also generates text and image content using a prompt. It analyzes input data to produce professional websites, web copies and images. If you want to know more about Generative AI check What is Generative AI.

Related Read: What is Prompt Engineering?

2. Search Engines

NLP is widely used in search engines as an intelligent technology that understands user's intent and queries to return relevant results, even if the keywords are not exact to the query.

For example, Bing AI’s Copilot, which is a powerful AI assistant that use Bing’s search engines to extend its capability to do more task.

Related Read: How to Use Bing AI?

3. Auto Translations

One of the major techniques of NLP is machine translation. 

Like Google Translate, which uses NLP to automatically translate any text or audio instantly.

4. Conversational AI

NLP is widely used in chatbots and virtual assistants. Where chatbots use NLP and Machine learning to understand queries and answer naturally to humans automatically. 

ChatGPT is a Conversational AI that responds to its users. 

If you want to know more about Conversational AI, check What is Conversational AI?

5. Automated Speech and Voice Recognition

NLP is used in applications where the human voice needs to convert into a form that the machine can understand easily. Such as automatic speech recognition (ASR) and speech-to-text (STT).

6. Fixes Grammar

NLP is used in grammar correction applications. Where it checks the spelling and fixes any grammatical errors automatically. Such as Grammarly and Quillbot which use NLP technology to fix their sentence grammar.

7. Autocorrect and Autocomplete sentences

NLP is used in applications to suggest any word missing in a sentence. Also, it can autocomplete any sentence by predicting the next word in the sentence.

8. Sentiment Analysis

NLP understands the sentiments and emotions of text data, which helps to pacify customers in different businesses.

9. Moderates Content

NLP uses Text Classification to classify the text of the content and detect whether it is spam or not, after which it will filter the text. It is used in Social media and online communities for content moderation.

10. Market Research

NLP is used in digital marketing industries to analyze customers' persona, conversation, and other data to gain insights about market trends and customer preferences.

What are the Main Challenges of NLP?

Although NLP is useful and helps to solve many problems through its application, there are some hurdles that NLP engineers are trying to overcome.

1. Ambiguity in Language

Human language has many different meanings for a single sentence, it is hard for the NLP model to understand the true meaning of the context. 

2. Understanding Sarcasm and Irony

NLP models use machine learning to understand a sentence by its definition or sentiments but struggle to detect its sarcasm and irony.

3. Depending on Trained Data

To make an NLP model effective, it needs to be highly trained on data. If there is lack of trained data it may provide unfair outcomes.

4. Understanding different terminologies

Each industry has its own set of terms for a specific word. Which is hard for an NLP model to distinguish.

5. Diversity of Languages

There are still so many languages in the world which NLP model still couldn’t grasp due to lack of resources.

6. Error and Misspelling

Error and misspellings in speech are often hard for NLP models to understand properly.

7. Limited reasoning ability

Although NLP models can process information and respond to prompts, they lack the logical reasoning ability to provide human conclusions.

What are NLP Tools?

NLP tools are software or development libraries that offer all the functionalities to analyze and process human language data.

There are mainly two types of tools used in NLP

1. Programming Libraries and Frameworks

These pre-written codes called libraries are used to create NLP applications. If you have expertise in programming you can create custom NLP applications.

Some framework-based NLP tools are: 

NLTK(Natural Language Toolkit)

It is a collection of Python libraries that can perform tokenization, tagging, lemmatization, and parsing.

SpaCy

It is an open-source Python library that can perform advanced NLP tasks efficiently using the Name Entity Recognition method, Parts of Speech tagging, and sentiment analysis.

Gensim

Gensim is a powerful open-source library that performs topic modeling to find hidden topics in text data and also locate similarities between documents using statistical methods.

2. Cloud Based NLP APIs

These are prebuilt services that developers can integrate into their applications through the cloud. Some  Cloud-based APIs for NLP tasks are

IBM Watson

IBM Watson is a powerful API tool that can understand human language and helps to build NLP applications using different algorithms.

Google Cloud Natural Language API

This is a great service that gives access to pre-trained NLP models developed by Google. You can create many different applications for different industries by performing all the NLP techniques.

Open AI Text generation model

OpenAI’s ChatGPT uses a text generation model which produces human-like text from its inputs. Trained on vast amounts of text data, that understands context-sensitive responses while producing coherent natural language text across a range of topics and styles.

Related Read: How to Use ChatGPT API: A Step-by-Step Guide

Final thoughts

Great you just learned What is Natural Language Processing, its use cases and the prominent tool's name. 

Well, NLP is nowadays a common jargon in the AI field and it is already present in many applications you use daily. It is the rising field in artificial intelligence. Although there are some challenges that will soon be overcome with proper research and training.