What is Natural Language Processing?

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. 

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. 

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.

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.

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 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. 

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.

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.

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.

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.