NLP Services | AIACME

Natural Language Processing in AI

Natural Language Processing (NPL). Conversant AI Is Another Term For This Technology, Which Is Also Known As Chat Machines. Whatever You Want To Call It, It's a Machine Learning Application That Teaches Computer Systems To Understand Human Speech. Up To 70% Of People Will Communicate Daily With Conversational AI Platforms In The Future, According To Research.

AI That Can Help

NLP-Based Technology Helps Businesses In Data Analysis, Insight Discovery, Process Automation, And Competitive Advantage.

Automatic text synthesis

Condensing A Text Into A Shorter Version Requires Identifying Its Key Concepts While Maintaining The Text's Intended Meaning. News Headlines Employ This NLP Technique.

Marketing Research

To Obtain Actionable Information That May Be Used For Strategic Decision-making, It Is Important To Gather Insightful Data On Trends, Customers, Goods, And Rivals.

Automatic TextCategorization

In The Search Phase, Information Can Be Quickly And Easily Retrieved By Assigning Tags To Text Based On Its Content And Semantics.

Automatic Grammar check

Another Important Component Of NLP Is Automatic Grammar Checking, Which Involves Identifying And Correcting Grammatical And Spelling Issues In Text Based On Context.

Challenges of NLP in AI

Increasing productivity

Automated help should be available around the clock to answer routine calls, with customer service staff able to handle more complex issues faster and with shorter response times.

Challenges of NLP in AI

Improve the customer experience

AI-powered virtual agents can deliver information faster and more accurately than humans.

Challenges of NLP in AI

Drive innovation

Detecting fraud, optimising the supply chain, and delivering cutting-edge goods and services - it's all possible with AI.

Challenges of NLP in AI

Reduce costs

Customer service can be very expensive. When trained properly, virtual assistants can handle more requests faster and increase relevance, accuracy and complexity over time.

Use Cases

Uses Case of NLP in AI

Sentiment Analysis

By Identifying Emotions In Text, Opinions Are Categorised As Either Positive, Negative, Or Neutral. You Can See How This Works By Adding Words To Our Free Sentiment Analysis Tool. Businesses Can Learn More About How Consumers Feel About Brands Or Products By Examining Social Media Posts, Product Reviews, Or Online Surveys.

Language Translation

Language Translation Tools Can Teach Translation Software To Understand Specific Technical Terms Used In Any Industry, Such As Banking Or Medicine. Translation Errors Common To General Translation Software.

Extraction Of Text

Text Extraction Is A Method Of Extracting Predefined Information From A Text. This Programme Helps You Identify And Extract Relevant Keywords, Features (Such As Product Codes, Colours And Specifications) And Named Entities When Working With Large Amounts Of Data (Such As Names, Locations, Companies, Emails, Etc.).

Chatbots

Chatbots Are Artificial Intelligence (AI) Systems Designed To Communicate With Humans Audibly Or In Text. As They Actively Learn With Each Interaction And Become Better At Deciphering The User's Intent, Chatbots Can Be Trusted With Simple And Routine Tasks: They Forward Customer Inquiries To A Human Representative When They Are Unable To Answer Them.

Topic Classification

You Can Classify Unstructured Text Using Topic Classification. This Is A Great Strategy For Businesses To Leverage Customer Feedback. With A Topic Classifier For NPS Feedback, All Your Data Will Be Tagged In Seconds. In Addition, You Can Use Topic Classification To Automatically Tag Incoming Support Tickets And Route Them To The Appropriate Person.