Artificial intelligence has become the focal theme of discussions related to technology. The impressive wonders of artificial intelligence have penetrated people’s everyday lives. Have you wondered how virtual assistants such as Alexa and Siri respond to your queries? You can thank Natural Language Processing or NLP for helping you request Alexa to play your favourite song.

NLP is a noticeable subdomain of artificial intelligence that enables computers to understand, generate, and modify human language. In the examples of virtual assistants, NLP helps them understand the requests of users and respond in natural language. Natural Language Processing is applicable to written text and speech and works for multiple languages. Let us learn more about natural language processing and its importance.

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Definition of Natural Language Processing 

Natural Language Processing is a subdomain of machine learning that helps computers with the interpretation, manipulation and comprehension of natural language. The next important aspect of any natural language processing introduction reflects on how it blends computational linguistics or rule-based modelling of natural language with machine learning and statistical models. The blend of computational linguistics with machine learning can empower computers and other digital devices to recognize, understand and generate text and speech.

The answers to the question “What is natural language processing?” revolve primarily around its significance in AI. It is the core element in applications and devices that can serve different types of use cases. For example, NLP can help computers develop capabilities for translating text into different languages and responding to text or spoken commands.

NLP also helps machines with voice-based recognition of users, assessment of intent or sentiment in text and speech and summarization of large volumes of text. Most important of all, natural language processing can help in completing all these tasks in real time. 

Some of the common examples of NLP include digital assistants, customer service chatbots, voice-operated GPS systems and speech-to-text dictation software. Natural Language Processing also serves a vital role in business solutions by streamlining and automating business operations. It also helps businesses with simplification of mission-critical business processes and enhancing employee productivity.

Why Should You Learn Natural Language Processing?

Natural Language Processing is a critical requirement for comprehensive and efficient analysis of text and speech data. Anyone who wants to learn NLP must familiarize themselves with the ways in which NLP transforms business operations. It can help in working through differences in grammatical irregularities, dialects and slang in daily conversations.

Businesses leverage NLP for different types of automated tasks such as processing, analyzing and archiving large documents or classifying and extracting text. Natural language processing can also help in a comprehensive analysis of customer feedback and call centre recordings. NLP also helps businesses answer common questions from customers and run chatbots to automate customer service. 

Another interesting use of NLP is the integration of NLP in customer-facing applications to facilitate effective communication with customers. You can dive deeper into natural language processing examples, such as chatbots in business applications. Chatbots can help analyze and sort customer queries and automatically respond to common questions.

On top of that, chatbots can redirect complex customer queries to customer service agents. As an expert in NLP, you can help businesses make the most of the value of automation in repetitive tasks. Therefore, you can explore new and lucrative career paths with fluency in NLP.

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How Can Businesses Use Natural Language Processing?

Businesses can leverage natural language processing tools and software for simplification, automation and streamlining operations with accuracy and efficiency. Here are some of the most notable examples of NLP use cases for business.

  • Customer Engagement 

This technologies help chatbots and voice bots replicate human-like interactions in their conversations with customers. Different business can leverage NLP uses in chatbots for scaling customer service and quality alongside maintaining operational costs at a minimum. Chatbot software developer PubNub utilizes Amazon Comprehend for offering localized chat functionality to international customers. Similarly, Oklahoma State University utilizes a Q&A chatbot for answering student questions with machine learning and NLP.

  • Business Analytics

The examples of natural language processing in business applications also encompass business analytics. Marketers utilize natural language processing or NLP tools such as Amazon Lex and Amazon Comprehend to develop a better impression of the customer’s perception of products and services by a company.

NLP tools can help in measuring the emotions and moods of customers in written feedback. One example of using NLP for business analytics is Success KPI. It offers NLP solutions that can help businesses to focus on specific domains of sentiment analysis. On top of that, NLP solutions can support contact centres by extracting valuable data from analytics.

  • Redaction of Sensitive Data

Businesses working in the domains of insurance, healthcare and law have to process, classify and extract large volumes of sensitive documents such as medical records, private data and financial data. Companies can use a natural language processing introduction to understand its potential for redacting personally identifiable information.

Businesses can use NLP to save time on the repetitive task of checking for users’ sensitive information and ensuring ideal safeguards. For instance, Chisel AI utilizes NLP to help insurance carriers extract policy numbers, expiration dates, and other customer attributes from unstructured documents. 

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Working Mechanism of NLP 

The next crucial detail in an introduction to NLP is the working mechanism. You can develop a better understanding of answers to “What is the natural language processing?” by exploring the core elements that build NLP. The important components of NLP include computational linguistics, deep learning and machine learning models for processing human language. Here is an overview of the important components of NLP work.

  • Computational Linguistics

Computational linguistics refers to the science of comprehension and construction of human language models with software tools and computers. Researchers utilize computational linguistics in the form of semantic and syntactic analysis to develop frameworks that can help machines in understanding natural human language. You can find prominent use of computational linguistics in tools such as speech recognition software, language translators and text-to-speech synthesizers. 

  • Machine Learning 

Most of the natural language processing examples also draw attention towards the significance of machine learning. Machine learning is a useful technology for training machines with sample data to improve efficiency.

Human language has different elements, such as variations in sentence structure, sarcasm, grammar, usage exceptions, and metaphors. Developers can use machine learning methods to teach NLP applications and improve their capabilities for recognizing and accurately understanding such features in natural language. 

  • Deep Learning 

Deep learning is a popular domain in machine learning that helps computers and machines to think and learn like humans. It is an important component in innovative NLP uses that leverages a neural network made of data processing nodes in a structure that resembles the human brain. Deep learning can help computers in recognition, classification and correlation of complex patterns in the inputs.

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Practices for NLP Implementation 

The implementation of NLP is an essential highlight in introductory guides to natural language processing. Any natural language processing introduction would emphasize the steps for collecting and preparing unstructured text and speech data from different sources.

NLP leverages data from sources such as surveys, cloud data warehouses, internal business process applications and emails. Here are the important steps you must follow for NLP implementation.

  • Pre-processing

Natural language processing software utilizes pre-processing techniques such as tokenization, stop word removal, lemmatization and stemming to prepare data for different applications. The pre-processing techniques for natural language processing or NLP help in creating the ideal foundation for training NLP models.

Tokenization helps in breaking a sentence into different units of phrases or words. Stop word removal ensures the removal of words that are irrelevant to the meaning of a sentence. Stemming and lemmatization work on simplification of words to their root firm.

  • Training 

The training process is a significant aspect of natural language processing and researchers utilize pre-processed data and machine learning for training NLP models. The training process helps NLP models in performing specific applications on the basis of text inputs provided to them. Developers can train NLP algorithms by feeding the software with large data samples for increasing the accuracy of algorithms.

  • Deploying the NLP Model

The final step in NLP implementation in any natural language processing introduction focuses on the deployment of the NLP model. Machine learning experts can also integrate the NLP model in a production environment.

The NLP models would then receive input and predict output for specific use cases tailored for the model. You can also run the NLP application with live data to achieve the desired output.

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Final Words

The basic introduction to NLP uses in business, and its working mechanism showcases how NLP can transform communication between machines and users. Natural language processing can help businesses automate and simplify redundant business operations, thereby improving productivity.

NLP expertise is a valuable asset in the modern labour markets in the domain of technology. Most important of all, you can find different perspectives on the uses of natural language processing with innovative developments in machine learning and AI. Learn more about the techniques and tool stack required for natural language processing now.

About Author

David Miller is a dedicated content writer and customer relationship specialist at Future Skills Academy. With a passion for technology, he specializes in crafting insightful articles on AI, machine learning, and deep learning. David's expertise lies in creating engaging content that educates and inspires readers, helping them stay updated on the latest trends and advancements in the tech industry.