What is Natural Language Processing?

What is natural language processing (NLP), how does it work and what does the future hold for this promising field? If you’re a tech enthusiast, you understand the importance of artificial intelligence (AI). It’s more than what you see in science fiction movies — it’s an emerging field of technology that describes machines with humanlike intelligence and capabilities.

With applications like NLP, your gadgets can better understand what you want from them, expanding the range of what AI can do. Ultimately, it’s breaking down the communication barriers between humans and machines.

What is NLP?

NLP is an AI component that helps computers interpret, analyze, understand and manipulate spoken and written language. Its roots go back to the 1950s when well-known mathematician Alan Turing published an article titled “Computing Machinery and Intelligence.” Turing also developed the Turing Test, a method of determining whether machines demonstrate human intelligence. 

ML, computational linguistics and statistical and deep learning models create NLP. It can reveal the structure and meaning of a text, in addition to gathering information about people, places and events. Additionally, machines with NLP can analyze and extract information from voice data.

Human speech is complicated. One word can mean any number of different things depending on its context. This nuance has been a stumbling block for past programs that could only recognize keywords. With NLP, programs can identify words and their meanings. This broader grasp of language allows machines to help humans in far more areas than ever.

How NLP Works

Natural language processing breaks down the human language into fragments. This allows the computer to analyze each fragment and determine a sentence’s structure. The fragments also allow the computer to analyze the words’ meanings. Here are some essential pre-processing tools data scientists need before they can use NLP:

• Tokenization: Breaks text down into small units
• Stop word removal: Filters out common words or phrases that offer little to no meaning
• Part-of-speech tagging: Marks words as nouns, verbs, adjectives, etc.
• Stemming and lemmatization: Reduces words to root forms for standardization

With these components, NLP can transform basic text into something meaningful like humans. After pre-processing, the machine needs an algorithm to process it.

Foundations of Natural Language Processing

NLP is divided into five primary steps, including the first step mentioned above. Fully understanding just one of these categories can be difficult for a program, so NLP is not a perfect process yet. Take a deeper look at these five stages to learn how it works.

Lexical/Morphological Analysis

The first stage is briefly mentioned above. It’s the initial stage of NLP and focuses on recognizing and analyzing word structures. Collections of words, a.k.a. phrases, are referred to as lexicons. During this stage, the machine searches for morphemes, which are small units of words. The words’ probably parts of speech are also analyzed in the lexical/morphological analysis stage.

Syntax Analysis/Parsing

Syntax analysis checks for grammar, arranges words and displays the relationships between words and phrases. This guarantees that the structure of a text and the grammar are correct. In simple terms, this stage involves the machine “making sense” of text.

Semantic Analysis

In the third stage, semantic analysis, the machine tries to find meaning within a statement. It concentrates on the meaning of each word and its dictionary definition. It can also involve putting words together to form sentences that make sense. 

Discourse Integration

Discourse integration refers to the feeling you get when you read a sentence. This means training a machine to provide content for any text in NLP applications. Sentences gain meaning based on their preceding sentences and establish the meaning of the following sentence. This also refers to the use of proper nouns and pronouns.

Pragmatic Analysis

The final NLP phase is pragmatic analysis. The content from the previous stages and its impact on interpretation are the focus of this stage. The machine uses a set of rules during pragmatic analysis to find an intended meaning. It can identify things like:

• Word repetition
• Who said what to whom
• How people communicate with one another
• The context in which people converse

Essentially, the fifth stage is the “meat and potatoes” of any NLP process because it relies on the four previous stages and results in a valuable output.

Natural Language Processing Examples

You probably encounter natural language processing regularly, even if you don’t realize it. If you’ve ever used Siri or Alexa, you’ve used a program driven by NLP. Here are some examples of it in action to help you understand how special this technology is for consumers and businesses.

Digital Voice Assistants

Small digital voice assistants are in millions of households worldwide. Different companies sell these devices, such as Google, Amazon and Apple. Amazon Alexa is probably the most well-known example of a device that uses NLP, as it is compatible with many smart home technologies. 

Email Filters

Spam emails are a nuisance, but NLP allows you to filter out unwanted messages so they land in your junk folder. Email filters are considered a basic application of NLP, but they have evolved and can do more than just recognize spam.

Spell Check

Spell check is a godsend for students, business professionals and virtually anyone who is responsible for typing essential documents. Spell check is popular in word processing software like Google Docs, Microsoft Word and Grammarly. 

Autocomplete

When you type an inquiry into Google, do you know how the search engine will provide predictions? This is another example of autocomplete. It makes it much faster and easier to type sentences, as software with NLP can guess what word will come next and allow the user to hit “tab” to finish.

NLP in the Future

As Natural language processing improves, it will become even more critical to our lives. It can help health care professionals analyze patient notes to understand their conditions better and how to proceed with treatment. Advanced systems may be able to recognize sensitive information and then improve security around it.

As more data travels online, advanced security like this will become increasingly crucial. Improved NLP will make smart assistants a more viable concept. Current versions like Siri are limited, but if you could converse with your digital assistant, they could be more helpful. Natural language processing has already revolutionized AI applications.

Expect Natural Language Processing to Evolve

Natural Language Processing is an interesting technology with numerous applications. Many tech companies leverage NLP and create helpful features for their products. Because it’s used for many different reasons, NLP will likely become more advanced. NLP will fine-tune communication between humans and machines and create a more digitally driven world.

Original Publish Date 3/14/2023 — Updated 12/18/2024