By: Poornima Muralidharan RIG Inc Intern Researcher


Industry 4.0 or in other terms the Fourth Industrial Revolution has become the driving factor for everything that is functioning in today’s world. As we all know, the industrial transformation or revolution started from the invention of the steam engine. It started to transform from steam power in the nineteenth century to electricity in the early twentieth century and automation after the 1970’s. The fourth revolution or industry 4.0 is driven by data and it can be called evolutionary more than revolutionary as the advancements in technology with big data have become an everyday story. Key advancements that industry 4.0 has introduced include Big data, Internet of things, Cloud computing, Robotics, Artificial Intelligence and Natural Language Processing (NLP) etc.

Natural Language Processing (NLP) is a field that was introduced as a result of the intersection of computer science, artificial intelligence and linguistics. The main goal behind introducing NLP is to make computers understand Natural language and perform human tasks like translating languages or answering questions. With inventions like chatbots and voice interfaces, NLP has become the most important technology of industry 4.0 and a popular field in AI.


Natural Language Processing is a form of artificial intelligence that enables a machine to read and understand human language. Everything we express (either verbally or in writing) carries large amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and subsequent value can be extracted from it. To be more precise, NLP when implemented accurately, can understand and even predict human behavior.

To understand human interactions or to converse with humans a program needs to understand syntax that indicates the grammar, semantics that denotes word meaning, morphology, the tense and pragmatics of the conversation. NLP uses a pattern learning based computer programming methodology and later started implementing Graphical Processing Units (GPU). It is largely comprised of two process

  • Human to machine -Natural language understanding
  • Machine to human-Natural language generation.

NLU mainly focuses on extracting information from large unstructured data in the form of videos, text, audio, images, social media data and customer surveys.

There are five common methodologies used in NLP:

  1. Named-Entity Recognition: This method is used to highlight the fundamental concepts by extracting the entities in the text.
  2. Sentiment Analysis: This method is widely used in situations like product reviews, customer surveys and social media comments.
  3. Text summarization: This NLP technique is used when large chunks of data need to be summarized and interpreted.
  4. Aspect Mining: This technique is used when different aspects from text needs to be derived and interpreted. This when used with sentiment analysis gives complete information from text.
  5. Topic Modeling: This technique is an unsupervised technique used to identify topics from the text.

When text is used for analysis, processing NLP can be divided into four distinct categories.

  • Distributional
  • Frame based
  • Model-Theoretical
  • Interactive Learning.


The main objective or purpose of NLP is to understand human languages in a way that is sensible and valuable by reading, deciphering and understanding the data. NLP has various purposes for which different applications are built. [1] A few important purposes of NLP are mentioned below.

  • Speech Recognition
  • Improvement in Clinical Documentation
  • Data Mining Research
  • Computer-assisted Coding
  • Automated Registry Reporting
  • Clinical Trial Matching
  • Prior Authorization
  • Clinical Decision Support
  • Risk Adjustment
  • Hierarchical Condition Categories


NLP has various techniques, purposes and thus, different applications: Siri or Alexa being examples of Text-speech synthesis, Grammarly being an example of Text summarization and chatbots serving as an example for Aspect mining. When 24*7 support needs to be provided chatbots are an ideal option. Chatbots act as a Question-Answer service empowered by Machine Learning. One such example is the Teslabot v2.0.


GOAL: Teslabot is a chatbot that in real time allows Tesla owners to control their Tesla cars.

FEATURES: The first version of Teslabot is a Facebook Messenger chatbot.[2] This was developed on top of Smartcar’s API. Elon is the personal AI assistant of Teslabot. These features include

  • Lock and unlock: Using this feature, cars can be locked and unlocked, replacing car keys.
  • Locate: This feature allows owners to know where the car is parked.
  • Read odometer: To check how many miles the owner drove directly from your phone.


After a quick glance at the concept of aspect mining under NLP, a chatbot that can answer users’ questions in real time can be built with RIG’s Dynamic Trust PwA. This can act as a messenger chatbot (Question Answer with Machine Learning) or a virtual assistant that can answer customers questions regarding the products of RIG. When implemented with Dynamic Trust PwA, this chatbot can secure customer’s data.

Figure 1: Chatbot benefits for business [3]

In general, chatbots carry many advantages that can contribute in the expansion of a business. Since it is available 24*7, it improves customer satisfaction and customers can utilize this feature for immediate support which can save lot of money. All these benefits put together can increase the overall sales of the business.

To learn more about Dynamic Trust from RIG please visit