XenonStack Recommends

Data Science

Natural Language Processing with Generative AI and LLM

Dr. Jagreet Kaur Gill | 22 November 2023

Evolution of Natural Language Processing with Generative AI

What is Natural Language Processing?

Natural Language Processing is a subset technique of Artificial Intelligence that is used to narrow the communication gap between the Computer and Human. It originated from the idea of Machine Translation (MT), which came into existence during the Second World War. The primary idea was to convert one human language to another human language, for example, turning the Russian language into English language using the brain of the Computers, but after that, the thought of conversion of human language to computer language and vice-versa emerged, so that communication with the machine became easy.

Simply, a language can be understood as a group of rules or symbols. These symbols are integrated and then used for transmitting and broadcasting the information. Here, rules are applied to suppress the signs. Natural Language Processing is divided into sub-areas, i.e., Natural Language Generation and Natural Language Understanding, which are, as the name suggests, associated with the generation and understanding of the text. The following chart broadly shows these points. Don't get confused by these new terms, such as Phonology, Pragmatics, Morphology, Syntax, and Semantics. Let's explore these in a very brief manner-

  • Phonology - This science helps to deal with patterns present in the sound and speeches related to the sound as a physical entity.
  • Pragmatics - This science studies the different uses of language.
  • Morphology - This science deals with the structure of words and the systematic relations between them.
  • Syntax - This science deals with the structure of the sentences.
  • Semantics - This science deals with the literal meaning of words, phrases, and sentences.nlp
The ability of machines to understand and interpret human language the way it is written or spoken. Click to explore about, NLP Techniques and Applications

What is the History of Natural Language Processing?

The history of NLP is described

The Beginning 

As stated above, the idea had emerged from the need for Machine Translation in the 1940s. Then, the original language was English and Russian. But other words, such as Chinese, also existed in the 1960s. Then, a lousy era came for MT/NLP during 1966; this fact was supported by a report of ALPAC, according to which almost died because the research in this area did not have the pace at that time. This condition became better again in the 1980s when the product related to it started providing some results to customers. After reaching a dying state in the 1960s, they got a new life when the idea and need for Artificial Intelligence emerged.


LUNAR was developed in 1978 by W.A Woods; it could analyze, compare, and evaluate the chemical data on a lunar rock and soil composition that was accumulating due to Apollo moon missions and can answer related questions. In the 1980s, computational grammar became a very active field of research linked with the science of reasoning for meaning and considering the user‘s beliefs and intentions. In the period of 1990s, the pace of growth of it increased. Grammars, tools, and Practical resources related to it became available with the parsers.

The research on the core and futuristic topics such as word sense disambiguation and statistically colored NLP the work on the lexicon got a direction of research. This quest for the emergence of it was joined by other essential topics such as statistical language processing, Information Extraction, and automatic summarising.

The First NLP Use Case Chatbot - ELIZA

The discussion on the history cannot be considered complete without mentioning ELIZA, a chatbot program developed from 1964 to 1966 at the Artificial Intelligence Laboratory of MIT. Joseph Weizenbaum created it. It was a program based on a script named DOCTOR, which was arranged for Rogerian Psychotherapists and used rules to respond to the users' questions, which were psychometric-based. It was one of the chatbots capable of taking the Turing test then.

The Government is taking modern emerging technologies in its architecture gradually. Click to explore about the role and Uses of NLP in Government

Applications of Natural Language Processing for Businesses?

Nowadays, everybody wants a machine to talk; a computer can only speak through it. Take the example of Alexa, a conversational product by Amazon. A query is passed to it by the voice medium, which can reply by the same medium, i.e., voice. It can be used to ask anything, search for anything, play songs, or make cab bookings. It seems to be magic, but it is not because of any magic spell; see the below diagram. This simple diagram demonstrates the procedure of Natural Language Processing in Alexa.

Alexa is not a single example, and these talking machines, popularly known as Chatbot, can even manage complicated interactions and processes related to streamlined business using it only. In the past, chatbots were utilized for only customer interaction with limited capabilities of conversation because they were generally rule-based, but after the emergence of Natural Language Processing Applications and its integration with Machine Learning and Deep Learning, chatbots can now handle many different areas, such as Human Resources and Health. healthcare

Health Care

Amazon Comprehend Medical services extract the disease conditions and can handle meditation sessions and monitor the treatment results using clinical trial reports, electronic health records, and patient notes. This is an example of NLP in health analytics where, using Language processing, the prediction of different diseases is possible using pattern recognition methods and patient speech and electronic health records.

Cognitive Analytics

This is the best example of the collaboration of different technologies, but both come under the same roof of Artificial Intelligence. The conversational frameworks are possible, which can take commands by voice or text. Using cognitive analytics, the automation of different technical processes is possible now, such as generating a technical ticket related to a technical issue and handling it in automated or semi-automated ways. The collaboration of these techniques can result in a computerized process of taking technical issues inside an organization or providing the solution of some technical problems to the customer in an automated manner.

Simulate the human thought process to learn from the data and extract the hidden patterns from data. Source: Cognitive Analytics Tools and its Applications

Sentiment Analysis 

Companies and organizations are now concentrating on the different ways to know their customers so that a personalized touch can be provided. The sentiments behind the words can be determined using sentiment analysis (which is possible only using NLP). The sentiment analysis can offer much knowledge about the customer's behavior and choices, which can be considered significant decision drivers.

Spam Detection 

The giants of the technical world, such as Google and Yahoo, use NLP to classify and filter out emails which are suspected to be spam. This process is known as Spam Detection and Spam Filtering, respectively. It results in an automated process that can classify the email as spam and stop it from entering the Inbox.


It can also be used in job recruitment search and selection phases. The chatbot can also handle job-related queries at the Initial level, including identifying the required skills for a specific job and taking initial-level tests and exams.

Conversational Framework

This technology and its related devices are gaining so much popularity these days. As illustrated above, Alexa is one of them, but there are Apple's Siri and Google‘s OK Google, examples of the same technology use cases.

A subset of Artificial Intelligence increasing its necessity with the improvement of its sub-technologies day by day. Click to explore about the applications of NLP For Businesses

Evolution of Natural Language Processing with Generative AI and NLP

Maturing technology is helping create more innovative NLP systems. Below are some key trends that are pivotal in shaping the future of NLP systems. Help shape the future of NLP systems. 

Smarter Service Desk Responses

Nowadays, when a user calls a service desk about a problem, they often get a ticket that has already been opened and will get a response within a few minutes. However, research shows that most tickets are repeatable and can be solved automatically if the organization knows how to mine NLU. NLU can be a big help and can be used to solve the problem quickly and automatically.

Once the NLP system analyzes previous interactions based on subject, content, and ticket category, it will decide how to resolve the issue. The first thing the NLP system will do is give the user a plan of action. Then, it will follow up the user’s email with the virtual assistant who can help solve the problem immediately. Receiving response emails will increase the customer’s relationship with the brand.

Enterprise Search with Generative AI and LLM 

Natural Language Processing can enhance the capabilities of enterprise software solutions. Most enterprise solutions collect and use vast data for everything from customer service to accounting. Many organizations invest significant resources to store, process, and get insights from these data sources. However, key insights and organizational knowledge may be lost within terabytes of unstructured data.

NLP improves team members and customer experience with enterprise software. It increases the interaction between the user and the software. 

 Natural language Generation with Generative AI and LLM

The NLG sub-section of NLP uses artificial intelligence to create stories from the data. NLG has six stages: Content analysis, Data analysis, Document structure, Sentence alignment, Grammatical structure, and Language presentation. Data filtering by identifying the key topics to include at the process's end. Machine learning is used to interpret and understand data. Based on the kind of data interpreted, a written plan is created.

Voice-driven Navigation Assistants

Voice control technology can be found in various product and service segments. It’s most commonly used in cars, where drivers rely on in-car voice assistants to perform multiple tasks. Some of the most common tasks that can be achieved with voice control technology include Setting navigation, Making hands-free calls, Making restaurant orders, Controlling in-car temperatures, and Operating windshield wipers and locks.

Leverage AI-based NLP capabilities for empowering Enterprises with sentiment analysis, information extraction, intent recognition, and text categorization solutions. Explore XenonStack's NLP Services

A Holistic Strategy to Build NLP Solutions 

To learn more about the uses and  Applications of Natural Language Processing in different domains we advise taking the following steps -