What is Natural Language Processing?

Most people acknowledge that artificial intelligence (AI) can perform some jobs, such as calculations and data analysis, better than humans. However, until recently, AI couldn’t compete when it came to complex, language-based tasks such as writing. 

But recently, that’s all changed with advances in natural language processing (NLP). NLP is a branch of AI that helps computers understand and process language like people do. Some things that are easy for people, such as recognizing sarcasm, are hard for computers. 

Despite these kinds of challenges in NLP, there have been new developments in machine learning recently. Large language models can now do amazing things, including coding programs and writing essays. NLP allows computers to understand human language and learn from unstructured data. 

Natural Language Processing (NLP) 101

For as long as we’ve been programming computers, we’ve had to communicate with them in their language. Computer language is extremely simple at its core — it all boils down to ones and zeros. Binary code is the language of computers. It’s used for everything from storing data files to changing the state of transistors on microprocessors. 

To say “Hello” in binary code, it’s “01001000 01100101 01101100 01101100 01101111 00100001.” So, while binary code is easy for computers to understand, it’s complicated for most people. 

That’s why programming languages were created: to make talking to computers easier. If you’ve ever tried coding, you may have found it difficult. But it’s far simpler to tell a computer what to do in Java than in binary code. 

Natural language understanding and processing flip the script so computers can speak to us in our language. Computers can use NLP to read text and understand speech. Then, they can interpret and analyze it to draw conclusions and perform tasks. 

What Is Natural Language Processing?

Natural language processing is rules-based modeling of the human language paired with statistical, machine learning algorithms and deep learning models. NLP enables computers to understand the meaning, intent, and sentiment of human language. 

Natural Language Processing Evolution

NLP began back in the 1960s with basic computer programs like the “ELIZA” chatbot. It was developed at MIT by Joseph Weizenbaum in 1966. ELIZA imitated a psychologist, but the processing was very simple. It used rule-based systems, such as repeating what people said. But it didn’t understand the meaning of the conversation.

In the 1970s and 1980s, programmers tried developing symbolic systems to handle NLP tasks. This worked for database querying and basic machine translation. However, it didn’t have many other uses. 

In the 1990s, developers shifted toward a statistical approach. In the early 2000s, data-driven machine-learning approaches became more popular. In the 2010s, deep learning and neural networks allowed AI natural language processing to perform more complex tasks. This is when natural language generation became much more useful.

Natural Language Processing and AI

AI is a broad umbrella term for an interdisciplinary field. It includes various subfields, including machine learning, robotics, and computer vision. NLP is a specialized area within AI that focuses solely on language-related tasks.

Natural language processing using AI developments, particularly machine learning and deep learning, has advanced the capabilities of NLP. Algorithms such as neural networks have transformed how machines understand language by capturing complicated patterns and relationships within text. These models can be trained on large datasets. They can perform tasks that would have been too complex for traditional, rule-based NLP systems.

In the 1990s, developers shifted toward a statistical approach. In the early 2000s, data-driven machine-learning approaches became more popular. In the 2010s, deep learning and neural networks allowed AI natural language processing to perform more complex tasks. This is when natural language generation became much more useful.

How Natural Language Processing Works

Natural language processing techniques combine algorithms, models, and operations that transform human language into data. 

The first step is called tokenization. It involves breaking down a chunk of text into smaller pieces, often called tokens. Tokens can be as small as characters or as long as words. Irrelevant characters like punctuation, special symbols, and numbers might be removed to simplify the text. However, some tasks, such as sentiment analysis, might keep exclamation marks as they can signify strong emotions.

The program then extracts features of the text using methods such as:

  • Bag-of-words: This method turns text into a vector where each element signifies the frequency of a unique word in the text.
  • Term frequency-inverse document frequency: This method weighs the importance of each term in the document and across a corpus. It often provides better performance than BoW for tasks like text retrieval.
  • Word embeddings: More advanced techniques like Word2Vec or GloVe produce word vectors that capture semantic relationships between words. These vectors can signify similarity, opposition, or other relationships in a multi-dimensional space.

Next, depending on the task, one or more of the following models is created to handle the data:

  • Rule-based algorithms: Early NLP relied on hand-crafted rules for text parsing or sentiment analysis. Today, rule-based approaches sometimes complement machine-learning techniques.
  • Machine learning methods: Algorithms like Naive Bayes, Support Vector Machines (SVM), and Random Forests can do certain NLP tasks, such as text classification and named entity recognition.
  • Neural networks and deep learning: Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are used for tasks that require understanding the sequence in the data, like machine translation. The Transformer architecture, and its derivatives like BERT and GPT, have set new standards in NLP performance in tasks ranging from text summarization to question answering.

Applications of Natural Language Processing

Much of our professional and personal lives occur online. Because of this, we generate a large amount of unstructured data. Structured data is stored in neat, easy-to-analyze databases. Unstructured data floats around in many different places. NLP helps organizations collect unstructured data to process and extract value from it. The following are some of the most common natural language processing examples.

Sentiment Analysis

Sentiment analysis determines if the tone of language is positive or negative. This job has been particularly challenging for NLP, as people use language in many ways. Someone might say, “I just loved waiting in line for two hours,” when obviously, they mean the opposite. 

Although sarcasm is still difficult in NLP, it analyzes many other types of language well. Businesses can use natural language processing software to analyze reviews or social media mentions. Then, they can tell whether customers like a particular product or feature. 
Newer semantic analysis models can also consider the context. A contextual semantic search crawls through thousands of messages and analyzes whether they’re positive or negative. It can also tell if they relate to a specific concept, such as price or customer experience


While AI-based chatbots have existed since the 1960s, they were primitive back then and relied on pattern recognition and pre-designed templates. Today’s chatbots use NLP for more open-ended conversations. NLP chatbots can understand input based on context and respond to customer feedback conversationally. 

Chatbots can automate many business functions. Employees are then free to handle high-value jobs. Chatbots can provide 24/7 customer support, personalization, and a quick response time. According to Gartner, by 2027, chatbots will be the primary customer service response for 25% of organizations. 

Speech Recognition

The initial stages of speech recognition often involve signal processing and feature extraction from audio data. NLP comes in when the system needs to understand, interpret, or act upon the spoken words. 

First, the program captures audio and extracts basic features. Then, the speech recognition engine typically converts the audio into a textual representation. This step is called automatic speech recognition (ASR). The output of ASR serves as the input for the NLP components. 

NLP uses language models to predict the likelihood of word sequences. This helps the system distinguish between similar words with different meanings. Once speech is converted to text, NLP techniques analyze sentence structure and meaning. This makes speech recognition systems smarter, more context-aware, and more capable of understanding and acting upon spoken language.

Healthcare Applications

Natural language processing applications have many uses in healthcare, including:

  • Clinical documentation: NLP takes valuable information from clinical notes and records. It then converts unstructured text into a structured format so it’s easier to analyze. Healthcare providers use this data to diagnose and treat patients more effectively.
  • Predictive analytics: NLP algorithms analyze patient records to predict outcomes, such as the likelihood of readmission or disease progression. These insights help clinicians make more informed decisions.
  • Telemedicine and virtual health assistants: NLP-powered chatbots and virtual assistants can handle initial patient screenings, answer medical questions, and even help manage chronic conditions. They do this by analyzing patient-reported data.
  • Public health: During outbreaks or pandemics, NLP can analyze social media, news, and other text data to track disease spread and public sentiment. This can help stop the spread of diseases.

Business Use Cases

Revenue from NLP is expected to reach $43 billion by 2025. NLP applications allow businesses to find hidden patterns in unstructured data, automate administrative tasks, reduce human error, and make data-driven decisions. Some of the most common business use cases include: 

  • Customer service automation: NLP-driven chatbots can handle customer questions, resolve issues, and provide information in real time. This improves customer satisfaction and reduces workloads for human customer service agents.
  • Market research and competitive analysis: NLP can analyze large amounts of text data, like news articles or public filings. Then, it can identify market trends or evaluate competitors. This can inform a company’s strategic decisions.
  • Resume screening: Human resources departments can use NLP to automatically scan and filter resumes. They can identify the best candidates based on keywords, experience, or other criteria. This speeds up the recruitment process.
  • Content recommendation: Ecommerce platforms or content providers can use NLP to understand user behavior and preferences. They can use this information to make personalized recommendations for products or content.
  • Fraud detection: By analyzing text in emails or communications, NLP algorithms can flag potentially fraudulent activities. This can help increase security.

Language Translation

NLP has led to advances in neural machine translation. Automatic translations in multiple languages can lead to increased accuracy, cost savings, customization, and consistency. Some of the best use cases for NLP in language translation are: 

  • Document translation: Businesses often need to translate legal contracts, user manuals, or internal documents to operate effectively in different regions.
  • Website localization: Companies can automatically translate their websites to appeal to customers across the world, increasing global reach and accessibility
  • Subtitle generation: Media companies use NLP to generate accurate subtitles in multiple languages for movies, TV shows, or online video content.
  • Real-time interpretation: NLP powers apps and devices that offer real-time translation services. NLP interpretation is useful in international meetings or travel scenarios
  • Tourism and hospitality: Translation apps or devices can assist tourists in navigating foreign countries, reading menus, or understanding local customs.

NLP Tools

NLP is a complex process that uses a combination of various tools, from programming languages to statistical and deep-learning models. Below are some of the most popular natural language processing tools.


Python is a popular programming language for NLP tasks. That’s because it’s simple, readable, and has an extensive ecosystem of libraries designed for text and language processing. Libraries like NLTK (Natural Language Toolkit), SpaCy, and TextBlob provide pre-built functions for tokenization, part-of-speech tagging, and sentiment analysis. In addition, machine learning frameworks like TensorFlow and PyTorch allow natural language processing with Python-based tools for building complex language technology models.

Statistical NLP

In the earlier stages of NLP, statistical models were the cornerstone for language processing tasks. These models use algorithms that rely on the frequency and likelihood of words and their combinations to understand and generate text. Techniques like Bayes’ theorem, Markov Chains, and n-gram models are commonly used in statistical NLP for tasks like text classification, machine translation, and summarization. Even today, statistical methods often serve as a starting point or baseline model for more complex NLP tasks.

Machine Learning and Deep Learning

Natural language processing in machine learning has transformed the capabilities of NLP. Algorithms can now learn from data rather than relying solely on hard-coded rules, making them more flexible and effective. 

Neural learning models like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are good at handling sequences. This ability is crucial for understanding temporal dependencies in language. Other machine learning approaches, such as the Transformer architecture, which powers models like GPT and BERT, have set new standards for NLP performance in tasks like language understanding, generation, and translation.

The Future of Natural Language Processing

The past few years have shown us how rapidly technology can evolve, and more advances are on the horizon. NLP models will likely become more adaptable when they encounter tasks they haven’t been exposed to in training. Called zero-shot and few-shot learning, these techniques will require fewer examples — or even no direct supervision. 

In few-shot learning, pre-trained models can generalize well from a small dataset. Then, they can accurately make predictions or classifications in cases they haven’t explicitly seen before. The “few-shot” term means that the model learns from a few examples — typically just one or a small handful for each data class or category.

Zero-shot learning takes this a step further. This approach allows a model to generalize to tasks without seeing any examples of that task during training. In zero-shot learning, the model uses its understanding of related tasks or the underlying structure of the data to make predictions on completely new or unseen categories. 

In addition to abbreviated learning models, here are some other trends and fields of research to watch:

  • Improved human-computer interaction: As NLP models become more sophisticated, we can expect more seamless and interactive forms of communication between humans and computers. Virtual assistants will likely become more conversational and context-aware, improving user experience.
  • Ethical and fair NLP: As NLP becomes increasingly widespread, there’s growing concern about the ethical implications, including issues of data privacy, algorithmic bias, and misinformation. Future developments in NLP will likely focus on creating more ethical and unbiased algorithms.
  • Explainability: As NLP models grow in complexity, there’s a need for these models to be explainable, especially in critical applications like healthcare, law, and public policy. Research in this area aims to make NLP models more transparent and understandable
  • Integration with other AI technologies: The merging of NLP with other fields like computer vision and robotics will likely lead to more robust and versatile AI systems. For instance, future robots could understand and process natural language commands while also interpreting visual cues.
  • Advanced sentiment analysis: Beyond understanding whether a statement is positive or negative, future NLP models may be capable of identifying more nuanced emotions like sarcasm, humor, or excitement, offering more depth in analysis

Leverage NLP and Artificial Intelligence To Improve Your Workflows

AI and NLP can take your business to the next level. These tools provide solutions to automate complex and time-consuming workflows and extract valuable insight from your data. 

Consensus Cloud Solutions offers a suite of NLP and AI-powered tools that are HIPAA-compliant to accelerate interoperability through API connectivity. With a 25-year history of innovating advanced products for tightly regulated industries such as healthcare, government, and finance, Consensus Cloud Solutions is a trusted global source for exchanging digital information. Reach out today to learn more.