NLP/ML – Machine Learning and Natural Language Processing Implementation
Natural Language Processing is a form of machine learning or artificial intelligence that aims to understand generate and analyze human speech and communication. Speech is a very creative, artistic and subjective aspect of human communication that is dynamic enough not to be predictable by mere grammatical rules. This makes the task of NLP challenging for machine learning. We see simple examples of machine learning and natural language processing implementation already the industry standard in several applications such as spam detection, autocorrect, voice assistance etc.
Advantageous of NLP & ML:
As compared to humans or automation-based computing alone, Natural Language Processing has several notable advantageous not just to the businesses implementing it but also to consumers using it. Some of these include:
Increased response speed
A vast majority of instructions, complains, navigation and feedback are reliant on some principles that can be taught to an intelligent software to process. Thus, using machine learning simplifies communications and increases the speed of delivering results.
Less resource consuming
A human oriented chat support for example, requires additional expenses, work environments and resources to house human employees safely. However, a machine learning natural language processing can run from a server alone.
Highly accurate predictions
A system based on analytical predictions results in faster and highly accurate predictions. These suggestions and predictions reduce wastage of resources and have resulted in targeted outcomes that appeal more to the consumer.
Reducing glut and spam
In the digital age of advertisements and mass bombardment of content and advertisements, the most important use of machine learning is to reduce the bulk glut of content and cater it only specifically to our use.
Real life applications
Natural Language Processing’s Implementation is across all facets of e-commerce and businesses to entertainment and consumer devices. These include:
- Extract and mining huge chunks of data for processing and analysis such as Google’s auto complete and most picked search results
- Autocorrect which is used in all word processing applications and smartphones runs on algorithm’s that base inputs to correct it to the most likely text instead.
- Translations: Today translation has become increasingly efficient to pickup even conversational language, thanks to NLP implementation.
- OCR: Optical character recognition has helped in graphic design, communications as well as enabling machines to interact with the environment around us much more seamlessly.
- Voice and handwriting recognition: The idea of giving voice commands or a smartphone’s camera being able to pick up handwriting and translate it or share information in real time would have seemed like fiction 10 years ago but it is rapidly becoming the industry standard today.
- Automated chat support: Its hard to imagine a business today that doesn’t utilize automated chat support that uses machine learning to interact with customers chatting in real life. It has replaced the need to hire hundreds or thousands of customer support agents and made interactions seamless.
- Spam filtration: Almost all email providers, web browsers and communication websites including social media now contain powerful spam filters that automatically detects and updates itself to filter out spam. As scammers and spammers try to find new ways to convince the user that their ad/posts are legit, such NLP implementation helps reduce the burden of cleaning out the mass posting of junk.
Natural Language Processing Implementation works hand in hand with machine learning to make our interactions easier, quicker and more accurate. As technology gets more and more accurate it would soon be hard to distinguish whether one is communicating with a bot or a human digitally. This would revolutionize the digital landscape as well as e-commerce and be a major boost for businesses worldwide.