What is Natural Language Processing and How Does It Work?

The ability to understand natural language has made machines capable of interacting with humans while they are taking a bank loan, checking the status of their online shipments, or scheduling an appointment with a therapist.

By 2029, the size of the NLP chatbot market is expected to reach $20.8 billion, growing at a CAGR of 24.3%—and this comes as no surprise. In just a dozen years, leveraging natural language processing for conversational tasks has become so common that until now every second consumer has interacted with a chatbot at least once.

What has propelled this technology to such widespread use? What’s running under the hood and how is it revolutionizing customer interactions? Let's delve into this topic right below.

What is NLP?

NLP, or natural language processing, is a branch of artificial intelligence (AI) that enables machines to understand, interpret, and generate human language. 

Just like that, speaking of NLP programming, we usually mean building systems that can process written or spoken language, allowing computers to perform tasks such as named entity recognition, machine translation, spam detection, or error correction.

Benefits of natural language processing for business

Considering that understanding and leveraging data is essential for businesses to thrive, NLP emerges as a powerful tool, enabling organizations to extract valuable insights and drive innovation. Here are some of the benefits the technology offers.

• Versatile data analysis 

NLP allows businesses to analyze various forms of data, such as speech and social media posts, providing comprehensive insights into customer preferences and market trends.

• Enhanced customer satisfaction

By employing sentiment analysis techniques, NLP helps businesses gauge customer sentiment accurately, enabling them to tailor products and services to meet customer needs effectively, thus increasing overall satisfaction.

• Cost reduction through automation
  

Leveraging NLP-enabled AI solutions automates repetitive tasks like customer interactions through chatbots, leading to significant cost savings by reducing manual effort and operational expenses.

• Deeper market understanding

NLP enables businesses to gain valuable insights into their target markets and brands by analyzing sources like social media posts and reviews. This empowers organizations to make informed decisions and drive strategic initiatives based on consumer preferences and market trends.

How does natural language processing work?

As we mentioned before, NLP deals with analyzing what people are saying and writing. Speaking technical language, NLP processes the utterances made in human language to identify the intent they contain, so that a machine can interpret them.

Let’s say a user types in “flying from New York to LA”—this is an utterance. If it comes to an NLP engine analyzing the intent of this query, it could be defined as “a trip” or “airplane tickets” depending on the context the NLP solution is implemented into. Once the intent is clear, it can be processed on the side of the app’s business logic to provide the user with a corresponding result, for example, tickets or route options.

Dmytro Romenskyi, Business Systems Analyst at Trinetix

No matter if the language is written or spoken, for a machine to understand what it’s about, it needs to be separated into fragments so that the grammatical structure of sentences and the meaning of words can be analyzed in the context provided. 

Once the text is pre-processed (transformed into something a machine can understand), the next step is building an NLP model and training it so it can interpret the language and perform specific tasks like sentiment analysis, named entity recognition, text summarization, and language translation—we’ll cover them in detail further in the article.

Through iterative training and fine-tuning, the NLP model becomes increasingly proficient at interpreting language and executing tasks with accuracy and efficiency, making it a valuable tool for a wide range of applications in fields such as healthcare, financial operations, customer service, and beyond.

To elaborate on this flow in more detail, let’s review the key data pre-processing techniques and algorithms that usually build up the NLP programming process.

Data pre-processing techniques in natural language processing

In their essence, NLP techniques refer to data pre-processing—the stage during which utterances are analyzed on the semantic level. In other words, these techniques help machines better recognize user intent by analyzing the components of the utterances.

Among the variety of techniques, there are some that are used most often. Let’s overview them below. 

Tokenization

Tokenization involves breaking down text into smaller units, or tokens, such as words or characters. For example, the sentence "I need to take a loan" would be tokenized into individual words: ["I", "need", "to”, “take", "a”,  “loan"].

Sentence Segmentation

Sentence segmentation involves splitting a block of text into individual sentences. For example, the paragraph "I need to take a loan. Tell me what type of documents I have to provide." would be segmented into two sentences: "I need to take a loan." and "Tell me what type of documents I have to provide."

Stop Word Removal

Stop word removal entails filtering out common words, known as stop words, that do not carry significant meaning in a given context. Examples of stop words include "the," "on," "is." For instance, in the sentence "The gym was closed for a bank holiday," stop word removal would eliminate "the," "was," for," "a."

Stemming and Lemmatization

Stemming and lemmatization techniques are used to reduce words to their base or root forms. Stemming involves removing prefixes or suffixes to obtain the word stem, while lemmatization considers the context of the word and reduces it to its dictionary form (lemma). For example, the word "running" would be stemmed to "run," while lemmatization would reduce it to its base form "run."

Now that we are done with understanding how natural language processing works and the techniques and approaches to it, let’s take a look at the algorithms that build up the foundation of an NLP model.

Traditional natural language processing algorithms

In contrast to semantic analysis techniques, NLP algorithms are computational procedures or methods designed to perform specific tasks related to language processing. 

While each of the algorithms mentioned above in practice has a number of variations, we can clearly define them into two categories: traditional natural language processing machine learning techniques and deep learning algorithms.

How do they work and what natural language processing tasks can they solve for businesses?

Logistic regression 

Logistic regression is a statistical method used to predict the probability of an event based on some input. In NLP, it can be used for tasks such as sentiment analysis or spam detection, where the goal is to classify text into two categories (e.g., positive/negative sentiment or spam/not spam).

Naive Bayes 

Naive Bayes is a classification algorithm based on Bayes' theorem. It assumes that features are independent of each other, hence the term "naive." In the context of natural language processing, it is commonly used for text classification tasks, such as document categorization or email filtering.

Convolutional Neural Network (CNN)

CNNs are a type of neural network architecture commonly used for image processing tasks. When it comes to natural language processing, CNNs can be adapted for tasks such as text classification or sentiment analysis.

Recurrent Neural Network (RNN)

RNNs are a type of neural network architecture designed to handle sequential data. In NLP, RNNs are commonly used for tasks such as language modeling, text generation, or sequence labeling (e.g., named entity recognition).

Autoencoder

Autoencoders in NLP use encoder-decoder architecture to compress text into a lower-dimensional representation, and then reconstruct it. This helps analyze text better by capturing essential features and reducing dimensionality.

Encoder-decoder sequence-to-sequence

Encoder-decoder models are a type of architecture used for sequence-to-sequence tasks, such as machine translation or text summarization. They consist of two separate neural networks—an encoder that processes the input sequence and a decoder that generates the output sequence.

Transformers

Transformers are a type of neural network architecture designed to handle sequential data more efficiently than traditional RNNs or LSTMs. They have achieved state-of-the-art performance on various NLP tasks such as language modeling, translation, and text generation.

As you see, NLP offers a robust set of techniques and methods that allow to approach language analysis and interpretation. And while the global goal of the technology is indeed understanding human language, there are plenty of tasks NLP models can solve to make it happen. 

At the same time, the above algorithms are examples of a conventional approach to NLP technology. This means that at present, they still make the foundation of advanced natural language processing mechanisms, yet in practice, none of them is any longer used as a separate technique.

Natural language processing: modern context

Today, the practitioner’s perspective of natural language processing is determined by two bright scientific phenomena of the 21st century:

1. The word2vec technique formulated by Tomas Mikolov in a paper called “Distributed Representations of Words and Phrases and their Compositionality” in 2013. Being short for “word to vector”, it represents a neural network-based algorithm that learns word embeddings—numerical representations of words that capture their semantic and syntactic relationships. As a result, NLP models equipped with word embeddings can perform a wide range of language-related tasks more accurately and efficiently.

2. “Attention Is All You Need”—a research paper proposing a new neural network architecture, published on Google Research in 2017 by Ashish Vaswani. This one, in fact, focuses on solving the limitations of the traditional sequence-to-sequence approach and RNN algorithms by reinventing the Transformer architecture and making it the basis of today’s most prominent natural language processing models like GPT and BERT.

At Trinetix, we are actively employing these modern practices while creating enterprise-scale NLP-empowered solutions that rethink traditional data classification practices, revolutionize personal digital assistance, and allow businesses to get maximum value from AI and ML adoption.

What is NLP used for?

Natural language processing can solve a variety of language-related tasks as a standalone technology. Among them, however, we would like to distinguish between the most practical ones. 

Below we are listing the technology applications that make up part of popular software applications we are using in our lives, sometimes not even knowing that NLP is enabled.

• Sentiment analysis determines the sentiment or emotional tone expressed in text data, helping businesses understand customer opinions and reactions.

• Toxicity classification identifies harmful or offensive language in text data, enabling platforms to moderate user-generated content and maintain a safe online environment.

• Machine translation automatically interprets text from one language to another, facilitating communication and breaking down language barriers.

• Named entity recognition identifies and extracts entities such as names of people, organizations, and locations from text data, aiding in information extraction and knowledge discovery.

• Spam detection deals with identifying unsolicited or unwanted messages in text data, helping to filter out spam emails, comments, or messages to enhance user experience and security.

• Grammatical error correction allows to automatically detect and correct errors in text data, improving the readability and clarity of written communication.

• Topic modeling allows to identify latent topics or themes present in a collection of text documents, assisting in organizing and exploring large volumes of textual data.

• Text generation implies creating new texts based on input data or predefined patterns and is useful for tasks such as generating product descriptions or personalized recommendations.

• Information retrieval refers to retrieving documents or information from a large dataset in response to user queries, enabling efficient search and access to information.

• Summarization allows to condense large volumes of text into shorter summaries while retaining the key information, aiding in information comprehension and decision-making.

• Question answering deals with generating answers to user questions based on text data, providing quick and accurate responses to inquiries in various domains.

NLP use cases across industries

On a global scale, the range of tasks NLP solutions can solve makes them extremely useful for activities like consumer feedback analysis, market research, customer support automation, and email processing. At the same time, employing the full potential of NLP for businesses requires building end-to-end solutions that we at Trinetix call intelligent digital assistants.

Intelligent digital assistants are conversational chatbots, digital platforms, and NLP-powered tools used to elevate business interactions, improve efficiency, and ramp up customer engagements. Empowered by deep learning and generative AI, they hold the potential to streamline company workflows, increase revenues, optimize cost-to-serve, and guarantee personalized experiences that put businesses ahead of the competition.

Dmytro Romenskyi, Business Systems Analyst at Trinetix

This type of digital assistants can apply to streamlining brand communications, assisting customers with their product or service-related requests, or providing personalized product recommendations. In addition, NLP makes the foundation of many translation tools, allowing users from different locations to access and interpret online information. 

This makes NLP-powered software solutions a fit for solving conversational tasks in a variety of industries and business departments.

NLP in Finance and Banking

• Conversational AI tools help customers with account inquiries, transaction history, and balance checks, providing personalized banking experiences.

• Intelligent digital assistants allow users to efficiently manage finances by offering budgeting tips, investment advice, and alerts for bill payments or fraudulent activities.

• NLP-powered assistants can also streamline internal processes by automating tasks such as data entry, compliance checks, and report generation for financial institutions.

Natural Language Processing in Healthcare

• NLP chatbots can help patients schedule appointments, refill prescriptions, and access medical records, improving patient engagement and satisfaction.

• Some conversational AI solutions can provide patients with information on symptoms, treatments, and preventive care as well as suggest addressing a desiccated specialist.

• Digital assistants can also support healthcare providers by automating administrative tasks, transcribing medical notes, and retrieving relevant patient information during appointments.

The Impact of NLP on Customer Operations

• Conversational AI chatbots handle customer inquiries, complaints, and support requests through phone, email, and chat, providing timely and accurate responses.

• Such NLP-powered tools can assist with order status inquiries, delivery updates, and troubleshooting technical issues, reducing customer wait times and improving service quality.

• Digital assistants can also analyze customer feedback and sentiment to identify trends, patterns, and areas for improvement in customer operations.

NLP in Travel and Tourism

• Chatbots assist travelers with itinerary planning, flight bookings, and hotel reservations, providing personalized travel recommendations based on preferences and budget.

• Intelligent assistants can offer real-time updates on travel disruptions, weather conditions, and local attractions, enhancing the travel experience.

• Such AI-powered solutions can also streamline operations for travel companies by automating customer inquiries, managing reservations, and processing payments.

Natural Language Processing in Retail

• NLP chatbots help customers find products, compare prices, and shop online, offering personalized product recommendations based on browsing history and preferences.

• For retailers, dedicated AI solutions can streamline inventory management, order tracking, and returns processing, improving operational efficiency and customer satisfaction.

• Intelligent assistants can streamline store operations by providing real-time updates on stock levels and facilitating order fulfillment processes, thereby improving efficiency and reducing operational costs.

NLP Applications in Sales and Marketing 

• Digital assistants can qualify leads, schedule appointments, and provide sales representatives with relevant information and insights about prospects.

• NLP-powered tools streamline lead generation, lead nurturing, and customer engagement through targeted messaging and personalized communication.

• Such conversational AI solutions can support marketing campaigns by analyzing customer data, segmenting audiences, and delivering tailored content across various channels to drive engagement and conversion.

NLP for Human Resource Management

• Conversational AI tools can help streamline recruitment processes by screening resumes, scheduling interviews, and conducting preliminary assessments of candidates.

• Intelligent assistants can improve employee onboarding, training, and development by providing resources, guidance, and feedback to new hires and existing staff.

• NLP-powered solutions can automate HR tasks such as time tracking, leave management, and performance evaluations, freeing up HR professionals to focus on strategic initiatives and employee engagement.

How to get started with natural language processing

When companies approach the development of conversational AI tools, there are a few ways in which they can employ the NLP technology:

• Integrating with NLP cloud solutions through a dedicated web API
• Using one of the SaaS products available in the market
• Building a solution from scratch using NLP libraries and frameworks

Although each of the options works fine in different situations and business scenarios, at Trinetix  we usually go for cloud solutions.

Integrating with web APIs like Google Dialogflow makes it possible to build custom NLP solutions that perfectly fit modern conversational business needs. They almost replace costly on-premise solutions by allowing engineers to build and deploy a model with necessary intents and train it using typical customer utterances.

Dmytro Romenskyi, Business Systems Analyst at Trinetix

What about the SaaS tools mentioned above? 

The majority of these solutions are indeed easy to use and require little programming knowledge, which makes them a fit for small businesses having basic requirements with NLP technology. 

At the same time, the lack of scalability and flexibility can significantly affect the results companies can get from its implementation. In addition, using some of the SaaS solutions still requires having a certain level of ML expertise. Finally, not all of the tools are familiar with deep learning concepts which can prevent businesses from getting the desired level of accuracy and customization.

These facts underline the importance of a reliable technology partner that can take over the entire NLP implementation lifecycle and seamlessly integrate technology into the existing IT infrastructure and company workflows—whether it’s a medium-sized business or an enterprise.

Approaching NLP software development in practice

So, what does it take to develop and implement a natural language processing solution?

1. First, it’s necessary to formalize business needs and assess the ways how they can be addressed. This is the stage where the decision to implement NLP is usually taken.

2. Next is the stage of technical analysis. Here a dedicated team assesses current infrastructure capabilities to define the bottlenecks and find the optimal way to integrate NLP capabilities.

3. The next step involves collecting and pre-processing text data for training and testing.

4. Once the infrastructure is set up and data is ready, it’s time to choose the suitable NLP algorithm to be used for building the NLP model.

5. The next step is training the model built using the pre-processing data and evaluating its performance using appropriate metrics.

6. If the performance is satisfactory, the trained model is deployed in production, either locally or in the cloud. 

7. Finally, the dedicated team is in charge of monitoring and updating the deployed model as needed and ensuring that the solution can handle large volumes of data and users.

This process, however, can be adjusted considering the scale and complexity of business needs and the resources available. What remains unchanged is the strategic approach that can guarantee a desired outcome.

Advantages you can get with Trinetix

At Trinetix, we are keen on exploring game-changing technologies and understanding the practical potential they hold for businesses. 

Our experience of conversational AI development roots years back and encompasses successful collaboration with market leaders. Acting as a dedicated technology implementation partner, we help companies:

• identify areas where NLP chatbots and digital assistants can drive the most value to business and customers
• boost employee productivity and operational efficiency by streamlining work activities and accelerating issue resolution
• drive deeper connections and unlock greater value from human interactions
• minimize organizations’ time and costs for AI chatbot development and support

If you feel like ramping up your business efficiency through personalized customer interactions, let’s chat about the ways how natural language processing can work for you.