What’s The Natural Language Processing?

What’s The Natural Language Processing?

Posted on: 21 Nov, 2023

Introduction

Today, Machine Learning And Deep Learning Areas Are One Of The Modern Industrial And Software Development Areas. With The Advancement Of Technology In Recent Years, Especially Hardware Developments Have Accelerated; For This Reason, Deep Learning Models Are Used More Easily And In An Optimized Way By Developers In Many Areas, One Of These Areas Is Natural Language Processing (NLP). 

What’s The Natural Language Processing?

Natural Language Processing (NLP) Is The Ability Of A Computer Program To Understand Human Language As It Is Spoken And Written, Referred To As Natural Language. It Is A Component Of Artificial Intelligence (AI). 

How Does Natural Language Processing Work?

NLP Enables Computers To Understand Natural Language As Humans Do. Whether The Language Is Spoken Or Written, Natural Language Processing Uses Artificial Intelligence To Take Real-world Input, Process It, And Make Sense Of It In A Way A Computer Can Understand. Just As Humans Have Different Sensors -- Such As Ears To Hear And Eyes To See -- Computers Have Programs To Read And Microphones To Collect Audio. And Just As Humans Have A Brain To Process That Input, Computers Have A Program To Process Their Respective Inputs. At Some Point In Processing, The Input Is Converted To Code That The Computer Can Understand. 

There Are Two Main Phases To Natural Language Processing: Data Preprocessing And Algorithm Development. 

Data Preprocessing Involves Preparing And "Cleaning" Text Data For Machines To Be Able To Analyze It. Preprocessing Puts Data In The Workable Form And Highlights Features In The Text That An Algorithm Can Work With. There Are Several Ways This Can Be Done, Including: 

Tokenization. This Is When Text Is Broken Down Into Smaller Units To Work With. 

Stop Word Removal. This Is When Common Words Are Removed From Text So Unique Words That Offer The Most Information About The Text Remain.

Lemmatization And Stemming. This Is When Words Are Reduced To Their Root Forms To Process. 

Part-Of-Speech Tagging. This Is When Words Are Marked Based On The Part Of Speech They Are Such As Nouns, Verbs, And Adjectives. 

Once The Data Has Been Preprocessed, An Algorithm Is Developed To Process It. There Are Many Different Natural Language Processing Algorithms, But Two Main Types Are Commonly Used: 

Rules-Based System. This System Uses Carefully Designed Linguistic Rules. This Approach Was Used Early On In The Development Of Natural Language Processing And Is Still Used. 

Machine Learning-Based System. Machine Learning Algorithms Use Statistical Methods. They Learn To Perform Tasks Based On Training Data They Are Fed And Adjust Their Methods As More Data Is Processed. Using A Combination Of Machine Learning, Deep Learning And Neural Networks, Natural Language Processing Algorithms Sharpen Their Own Rules Through Repeated Processing And Learning. 

NLP Use Cases

How Companies Use NLP 

 The Amount And Availability Of Unstructured Data Are Growing Exponentially, Revealing Its Value In Processing, Analyzing, And Potential For Decision-making Among Businesses. Nlp Is A Perfect Tool To Approach The Volumes Of Precious Data Stored In Tweets, Blogs, Images, Videos, And Social Media Profiles. So, Basically, Any Business That Can See Value In Data Analysis Will Find NLP Useful.  

Advanced Systems Often Include Both NLP And Machine Learning Algorithms, Which Increase The Number Of Tasks These Ai Systems Can Fulfill. In This Case, They Unpuzzle Human Language By Tagging It, Analyzing It, Performing Specific Actions Based On The Results, Etc. Think Of Siri Or Alexa, For Example. They Are AI-Based Assistants Who Interpret Human Speech With NLP Algorithms And Voice Recognition, Then React Based On The Previous Experience They Received Via ML Algorithms. 

To Dive A Bit Deeper, The Role Of Machine Learning For Natural Language Processing And Text Analytics Lies In Improving NLP Features And Turning Unstructured Text Into Valuable Insights. So, A Common Approach Looks Like This: You Train A Model To Perform A Task, Then Verify The Model Is Correct And Apply It To The Problem. Here Are The Main Tasks Fulfilled With The Help Of NLP. 

Search Autocorrect And Autocomplete 

Whenever You Search For Something On Google, After Typing 2-3 Letters, It Shows You The Possible Search Terms. Or, If You Search For Something With Typos, It Corrects Them And Still Finds Relevant Results For You. Isn’t It Amazing? 

Language Translator 

Have You Ever Used Google Translate To Find Out What A Particular Word Or Phrase Is In A Different Language? The Technique Behind It Is Machine Translation. 

Social Media Monitoring 

More And More People These Days Have Started Using Social Media For Posting Their Thoughts About A Particular Product, Policy, Or Matter. These Could Contain Some Useful Information About An Individual’s Likes And Dislikes. Natural Language Processing Comes To The Rescue Here Too. 

Chatbots 

Customer Service And Experience Are The Most Important Thing For Any Company. It Can Help The Companies Improve Their Products, And Also Keep The Customers Satisfied. But Interacting With Every Customer Manually, And Resolving The Problems Can Be A Tedious Task. This Is Where Chatbots Come Into The Picture. 

Survey Analysis 

Surveys Are An Important Way Of Evaluating A Company’s Performance. Companies Conduct Many Surveys To Get Customers’ Feedback On Various Products. This Can Be Very Useful In Understanding The Flaws And Helping Companies Improve Their Products. 

Targeted Advertising 

Imagine You Were Searching For A Mobile Phone On Amazon, And A Few Minutes Later, Google Started Showing You Ads Related To Similar Mobile Phones On Various Web Pages. Targeted Advertising Is A Type Of Online Advertising Where Ads Are Shown To The User Based On Their Online Activity. Targeted Advertising Works Mainly On Keyword Matching. 

Voice Assistants 

A Voice Assistant Is Software That Uses Speech Recognition, Natural Language Understanding, And Natural Language Processing To Understand The Verbal Commands Of A User And Perform Actions Accordingly. Today, Most Of Us Cannot Imagine Our Lives Without Voice Assistants. 

Grammar Checkers 

This Is One Of The Most Widely Used Applications Of Natural Language Processing. Grammar Checking Tools Like Grammarly Provides Tons Of Features That Help A Person In Writing Better Content. 

E-Mail Filtering 

For Instance, If You Received An Email On Gmail, Then You Might Have Already Noticed That Whenever A Mail Arrives, It Gets Classified Into The Sections Of Primary, Social, And Promotions. And The Best Thing Is That The Spam Emails Are Also Filtered Out To A Separate Section. 

NLP In The Turkish Language

In NLP Literature, Most Operations In English Have Been Carried Out With High Success Rates. Although There Are Some Developments In Other Languages, There Are Tasks That Can Contribute To The Literature, Especially In Turkish Languages. Especially Agglutinative Languages Such As Turkish Make Nlp Tasks Even More Difficult. 

Distribution Of Turkish Speakers Worldwide: 

Morphological Analysis Is A Very Important Sub-task Of Natural Language Processing. It Is Used For Tokenization, Stemming, Lemmatization And Normalization. For The Nlp Task In Which The Machine Learning Approach Plays A Crucial Role, Pre-processing The Data Is Vital And The Success Rate Is Highly Dependent On The Pre-Processing Methodologies.  

NLP Marketplace For Industry 

The Global Natural Language Processing (Nlp) Market Was Valued At Usd 10.72 Billion In 2020, And It Is Expected To Be Worth Usd 48.46 Billion By 2026, Registering A Cagr (Compound Annual Growth Rate) Of 26.84% During The Forecast Period (2021-2026). Due To The Ongoing Covid-19 Pandemic, The Market Is Witnessing Growth In The Healthcare Sector. 

Trends 

Large Organizations Are Expected To Register A Significant Growth;  

Large Organizations Are One Of The Primary Drivers And Investors In The Nlp Market. As These Organizations Are Increasingly Adopting Deep Learning, Along With Supervised And Unsupervised Machine Learning Technologies For Various Applications, The Adoption Of Nlp Is Likely To Increase. Cost And Risk Are Some Of The Major Factors Driving The Adoption Of These Technologies Among Large Organizations. 

Most Of The Large End-User Organizations Across Various Industries Are Mainly Utilizing These Technologies To Enhance Their Internal And External Operations. Moreover, The Roi Of The Technology Is Not Always In The Monetary Form. 

Moreover, Large-Scale Social Media Platforms Are Also Utilizing Text Analytics And NLP Technologies For Monitoring And Tracking Social Media Activities, Such As Political Reviews And Hate Speeches. Platforms Like Facebook And Twitter Are Managing The Published Content With The Help Of These Tools. 

North America To Hold The Largest Market Size During The Forecast Period 

The NLP Market Has Been Segmented Into Five Regions: North America, Europe, Apac, Mea, And Latin America. Among These Regions, North America Is Projected To Hold The Largest Market Size During The Forecast Period. Improvements In Cloud Computing Platforms, Which Are Now More Efficient, Affordable, And Capable Of Processing Complex Information, Have Led To The Growth Of Inexpensive Software Development Tools And Plentiful Datasets, Which Play A Vital Role In The Development Of Ai Technology In The Us Market. Apac Is Expected To Grow At The Highest Cagr During The Forecast Period On Account Of The Rising Awareness And Increasing Ai Investments. 

NLP Researches In WesterOps 

Companies Use Multiple Applications And Platforms On A Daily Basis To Manage Their Operations. The Context Switch Between These Applications Causes Distractions For Employees. We Propose A Chatbot Integrated Into The Main Workspace Of A Company As A Quick Interface To Multiple Applications To Prevent Employees From Switching To Other Applications. Our Published Article Presents Our Experience Building A Chatbot Prototype, Demonstrating Its Usage For A Human Resources Application, And Providing A Use Case For Requirements Engineering Activities. Plus, Our Digitalworkspace Project Will Contain Task Management Automation, Customer Sentiment Analysis, And The Superapp Concept. We Will Use The Aforementioned Nlp Techniques In Our Digitalworkspace Project In Order To Facilitate The Daily Workload Of Employees. 

We Organized Interviews With Our Company For Determining The General Features And Roadmap Of The Project, Ux/ui, Employee Expectations And Etc. The Most Important Requirements Were Listed For Initiating Stage. 

Although We Aim For A Chatbot That Is Connected To Multiple Applications Through Their Apis, We Choose A Human Resources Application As Our Pilot Application. In The Figure, We Provide The Use Case Diagram Of The Chatbot Only For Cases Related To The Human Resources Application. The User Can Type Messages Via The Chatbot Gui In Order To Communicate With The Chatbot. The Purpose Of These Messages Can Be Enhanced In The Future. Our Focus Is To Maintain The Hr Department's Daily Workload. The User Will Be Able To Create A Leave Request, List Leave History, List Devices, List Employees, Task Assignment, List Tasks, Payment History, Spending History And Etc. The User Needs To Approve The Request At The End. 

Use Case For Requirement Engineering (RE) Applications 

Collecting And Analyzing User Feedback Are Two Re Activities That Can Be Facilitated With Our Chatbot In The Future. Instead Of Providing The Customers With Forms Or Interfaces That Can Be Intimidating Or Assigning A Human That Can Be Costly, We Plan To Deploy A Chatbot For Collecting Issues Related To Our Software And Collect Necessary Information About Bugs And Feature Requests Using A Chatbot. 

In This Project, We Are Planning To Use A Multi-Tenant Structure For Increasing Security. In Addition, It Is Aimed To Establish An Automatic Structure As Much As Possible With Artificial Intelligence And Rule-based Algorithms. 

Conclusions 

In Conclusion, We Introduce An NLP-based Chatbot And Nlp-based Re Solutions To The Fatigue Caused By Multiple Platforms In Business Environments. Companies Use Multiple Platforms To Run Their Operations Which May Not Be Excelled By Their Employees And Customers. We Propose Integrating A Chatbot Integrated To The Super App Of The Company. We Also Provide A Re-related Use Case As Part Of Our Ongoing Work.