If you’re familiar with AI, chances are you’ve heard of natural language processing. In this guide, we answer the question: what is NLP, and how does it work?
Along with helping solve problems and managing large quantities of data, one of the main goals of artificial intelligence is to mirror human intelligence. One of the subfields of AI, natural language processing (NLP), was created to push that goal forward through creating AI that can mimic human language and speech.
While the technology has come a long way in the decades it has been around, it still has a ways to go in terms of functionality and practicality. However, the applications of natural language processing are growing, making it important for organizations to understand this quickly evolving tech. In this guide, we’ll answer the question: what is NLP, and how can it be beneficial in business?
What is NLP?
Natural language processing is the field of technology that’s working to help computers understand the human language. Computer scientists focused on NLP are not only interested in helping computers understand the hows and whys of natural human language—in some cases, they’re also interested in giving computers the capabilities of ‘speech’.
A combination of computational linguistics and machine learning techniques provides the foundation necessary for computers to successfully process and understand natural language through text or audio. With that foundation secured, computers can begin the process of understanding the intent and emotions behind the speech as well.
Of course, the ability to understand human speech lends itself to a variety of applications, from the mundane to the more mission critical for individuals and businesses. Below are some of the most common applications of NLP.
Common applications of natural language processing
Each year, natural language processing technologies grow their capabilities to encompass more applications, including:
Voicemail to text transcription: Most smartphones today offer this type of transcription, where NLP listens to and transcribes your voicemails.
Customer service chatbots: While many chatbots today operate off a pre-written script, AI-powered chatbots can help tackle more complex customer requests.
Seamless translation: From travelers to government officials, seamless voice to text translation (or vise versa) can always come in handy.
Voice-operated GPS systems: If you have a newer GPS system, you’ve likely input your destination via voice. NLP helps your GPS parse through your language.
Customer sentiment analysis: Brands looking to maintain a good reputation and resolve issues with customers can utilize sentiment analysis to better understand what feelings customer reviews and comments are conveying.
Textual analysis: There are a variety of applications of text analysis, not limited to: fraud detection, investigative discovery for crimes, and simple spam filtering.
How does natural language processing work?
Think back to elementary school. Did your teacher ever give you a sentence and ask you to label the verbs, adjectives, nouns, prepositional phrases, or more? That’s a simple way of understanding what computers do when they attempt natural language processing.
NLP works by pulling apart sentences and phrases into their most basic elements. Once the smaller parts are understood, the computer can then piece together the larger overall meaning of the text or audio.
In order to parse out human speech, computer scientists have employed a variety of methods to give machines NLP abilities.
First, in order to be able to understand the data it’s given, computers must be able to preprocess that data. This cleans up the data in a way that makes it the most ‘presentable’ for the computer to work with. There are a variety of ways that preprocessing can happen, including:
Part-of-speech tagging: Words are marked as their part-of-speech, including noun, verb, adjective, etc.
Tokenization: Text is broken down into smaller, more workable units.
Stop word removal: Common words are taken away from the text, leaving unique words that offer more meaning.
Lemmatization and stemming: Each word is reduced to its root form to make processing easier.
Once the data has been preprocessed, the computer system can then move to one of two methods to process the text. Early NLP systems utilized rules-based systems based on human linguistics to understand text. While this approach is still used, it’s not easily scalable, and fails to account for the seemingly endless exceptions to the rules that pervade human language.
To account for those exceptions and provide a more scalable model, many natural language processing systems today are built on machine learning or deep learning algorithms. This is called statistical NLP.
Through statistical NLP, each element of a text or audio file is organized and given a statistical likelihood of each possible meaning. The goal of this method is to help the algorithms learn and get better at understanding with each new data set.
Challenges associated with NLP
NLP has certainly come a long way in the past 50 years, but the technology is still working to overcome several obstacles.
For one, the human language isn’t stagnant, but ever-changing. Algorithms have to be constantly learning and updating in order to keep up with the pace of change. What’s more, spoken language is often riddled with slang, irony, homonyms, and different dialects/inflections, making it difficult to pin down exact meanings without human input. And for natural language processing to reach its potential, it needs to continuously process more and more data—something that can be costly and time-consuming for all but the largest organizations.
Next steps for natural language processing
As NLP continues to grow as a field of research and development, we’ll likely see this technology continue to grow in capabilities and applications. Businesses are quickly seeing the importance of this tech, and are investing more time and resources into training algorithms with large collections of data. And with more data available than ever before, it won’t be long before these systems can be fed a continuous stream of information.
NLP has already transformed the way we interact with the machines around us, and as it continues to grow in popularity, NLP will inevitably become a defining technology.
Curious to learn more about how new artificial intelligence technology can impact your business? We maintain a large AI affiliate network of companies that can help you bring NLP and other emerging technologies to your organization. Reach out today to learn more.