Natural Language Processing (NLP) is an artificial intelligence field that enables machines to interpret, understand and infer meaning from human languages. It is a discipline that focuses on the interaction of data science and human language and scales across many industries. Today, NLP is booming due to the enormous changes in data access and the rise in computational power, which helps practitioners to produce meaningful results in fields such as education, media, banking, and human resources.

NLP is thriving within the healthcare industry. New technology improves patient delivery, diagnosis of disease, and reduces costs as healthcare facilities are rapidly embracing electronic health records. The fact that it is possible to improve clinical documentation implies that patients can be better understood and benefit from better healthcare. The aim should be to improve their experience, and this is already being achieved by several organizations.
What is natural language processing?

Natural Language Processing (NLP) is a sub-field of computer science, linguistics, information engineering, and artificial intelligence. All these technologies have known to be related to computer and human (natural) language interactions, especially how computers are programmed to process and analyze large amounts of natural language data.

Governments' ability to use these innovations in language processing defies restriction. Massive numbers of unstructured documents are held by governments. For the kind of deep-pattern analysis traditionally reserved for computational databases, fragmented information presented in fundamentally non-mathematical formats is now accessible at volumes far too large for human evaluation. NLP can open up new insights, more tailor-made services and faster responses to information to the public sector.

Some use cases that show the power of NLP in the present era

• Natural language processing in healthcare

Medical services that are used to extract the conditions of the disease can handle meditation sessions and monitor treatment outcomes using clinical trial reports, electronic health records, and patient notes. This is an example of NLP in health analysis where it is possible to use NLP to predict various diseases utilizing pattern recognition methods and speech of patients and their electronic health record.

• Sentiment analysis using natural language processing

Companies and organizations are now focused on various ways to get to know their customers in order to provide personalized interaction. The emotions behind the words can be calculated by using sentiment analysis (which can only be achieved using NLP). The feeling analysis has the ability to offer a lot of knowledge about the actions of the consumer and their decisions that can be taken into account as critical decisive factors.

• Cognitive Analytics and natural language processing

This is the best example of different technologies working together, but both fall under Artificial Intelligence's same roof. The conversational systems that can take commands through the voice medium or the text medium are feasible using natural language processing. Using cognitive analytics, this generation of a technical ticket related to a technical issue is now possible to automate various technical processes and handle it in an automated or semi-automated manner. The application of these technologies can lead to an automated process of managing technical problems within an organization or can also provide the customer with an automated solution to certain technical problems

Trends that drive the natural language processing industry

1. Unsupervised and supervised learning

It is a common fact that machine learning provides significant support for natural language with applications of both supervised and unsupervised learning, particularly in text analytics. Once the word in a document is understood by Natural Language Processing and its parts of speech, unsupervised learning may establish mathematical relationships. Supervised learning is then based on the product of the relationship determinations of unsupervised learning.

2. Reinforcement learning

By reinforcement learning, a variety of natural language generation (NLG) tasks, such as text description, are being explored. Although reinforcement learning approaches show potential outcomes, they require appropriate action and state-space management, which may restrict the model's significant power and learning skills.

3. Deep learning

The support of natural language through Deep Learning is as significant as it is multifaceted. Techniques such as Recurrent Neural Networks will leverage to provide a very accurate classification for the use of parsing results and thus gain common grip in certain text analytics platforms for classification of documents and labeling of entities.

4. Semantic search

Another development expected to significantly revolutionize natural language and machine learning in the coming year is the need for semantic search. The search involves both the interpretation of natural language and the comprehension of natural language and necessitates a precise understanding of the central ideas in the text. Organizations that want to search through their document collection need the intelligence that comes from Natural Language Processing and Machine Learning into a search-based framework. This not only helps to inject back into operations but also to develop smart search or semantic search applications.

5. Cognitive communication

Text analytics is anticipated to remain the most extensive natural language use case in the years to come. Nonetheless, in use cases involving speech-to-text, smart chatbots, and semantic search, these technologies will also become more popular. Instigated by deep learning frameworks, unsupervised and supervised machine learning, the proliferation of natural language technologies will continue to influence cognitive computing's communication ability.

The big picture

The natural language processing market is evolving with a wide range of growth factors such as increased adoption of smart devices and the increase in technological investments in the healthcare environment. Developments in the processing of natural language have data governance connotations. It collects enormous amounts of user data, posing critical legal issues regarding data ownership, privacy, and protection.
Big tech companies like Google, Facebook, Microsoft, Amazon, and others will take over more control of what we see and do, but they are not the government and for governments to be successful, new regulations on how data is collected and disseminated via NLP need to be developed, specifically where Natural Language Processing is linked to financial gain.