It seems like artificial intelligence (AI) is all around us generating photos, music, and writing. Whether you realize it or not, you've almost certainly encountered and used AI yourself in the form of natural language processing (NLP). While this is a cutting-edge area of computer science, it also has many real-world applications that can help grow your business, save money and time, and do what you do even better!
We're here to walk you through what NLP is, what it does, and how you can put it to work for you.
What is natural language processing (NLP)?
Natural language processing (NLP) technology is a subset of computational linguistics, the study and development of algorithms and computational models for processing, understanding, and generating natural language text.
It's the technology behind virtual assistants like Siri, smart speakers like Alexa, and chatbots or other customer service tools that lets you ask a question in "natural language" (in a natural, conversational way rather than formatted as a search query) and then directs you to the right person or information.
Natural language processing tools have found widespread applications in several industries and scientific disciplines:
- Finance: Firms use NLP tools to analyze large amounts of research and gain insight into financial markets.
- Retail: Retail companies use sentiment analysis to monitor and understand feedback from social media without having to read every post.
- Medicine: Records can be summarized and analyzed quickly to find patterns and improve health care.
How does natural language processing work?
Natural language processing is a type of machine learning in which computers learn from data. To do that, the computer is trained on a large dataset and then makes predictions or decisions based on that training. Then, when presented with unstructured data, the program can apply its training to understand text, find information, or generate human language.
For example, a natural language algorithm trained on a dataset of handwritten words and sentences might learn to read and classify handwritten texts. After training, the algorithm can then be used to classify new, unseen images of handwriting based on the patterns it learned.
Most NLP programs rely on deep learning in which more than one level of data is analyzed to provide more specific and accurate results. Once NLP systems have enough training data, many can perform the desired task with just a few lines of text.
Machine learning methods
NLP applications are trained using machine learning algorithms. These training methods help the program understand the structure, meaning, and use of human language. Many NLP programs use machine learning methods that resemble the field of classical linguistics in which researchers focus on the structure of a language.
There are different types of learning methods for an NLP computer program:
- Supervised learning: The NLP program is trained on a set of data that's already labeled, for example, with text of a particular sentiment (positive, negative, or neutral) or one that falls into a specific category. The algorithm then learns from the data to match its results as closely as possible to the labeled results. Once the NLP software is programmed, it can apply the same analysis to new, unlabeled data.
- Unsupervised learning: Unlike supervised learning, this method lets the program learn on its own, using large amounts of data and statistical methods to analyze, understand, and create natural language. It's a type of deep learning in which a program extracts multiple layers of information from a set of data to improve its own analysis.
Natural language understanding
Whether it's analyzing online customer reviews or executing voice commands on a smart speaker, the goal of NLP is to understand natural language. Many NLP programs focus on semantic analysis, also known as semantic parsing, which is a method of extracting meaning from text and translating it into a language structure that can be understood by computers.
More training data, better results
Because NLP works to process language by analyzing data, the more data it has, the better it can understand written and spoken text, comprehend the meaning of language, and replicate human language. As computer systems are given more data—either through active training by computational linguistics engineers or through access to more examples of language-based data—they can gradually build up a natural language toolkit.
Statistical NLP is also the method by which programs can predict the next word or phrase, based on a statistical analysis of how those elements are used in the data that the program studies.
Techniques and methods of natural language processing
To understand what NLP can do, it's helpful to take a brief look at how it works and how it's driven by machine learning and deep learning models.
Text separation and understanding
NLP tasks break down text, segmenting it into smaller components that can be analyzed. Some of these techniques include:
- Word segmentation: This task divides sentences into individual words, which can be analyzed as units of meaning. In many languages, written words are separated by spaces, making word segmentation relatively straightforward. However, in some languages, such as Chinese, words are not separated by spaces and the process is more challenging.
- Word sense disambiguation: Many words have different meanings depending on how they are used in a sentence. While it may be simple for you to tell the difference between "he has good taste" and "the soup has a good taste," a computer needs to be trained on all the possible meanings of a word and which one makes the most sense in a given sentence.
- Part-of-speech tagging: Each word in a selection of text is labeled as a part of speech like a noun, a verb, or an adjective. This helps the NLP program understand a word's relationship to the words around it and the meaning of the overall text.
- Dependency parsing: This technique analyzes sentences to determine the relationships between phrases, helping the program understand the grammar and meaning of the sentence.
As the term implies, text extraction is used by NLP programs to look through a large amount of data to pull out relevant information using techniques such as named entity recognition to gather and categorize specific information like organizations and addresses. The extracted text can also be analyzed for relationships—finding companies based in Texas, for example.
This is the task of assigning labels to an unstructured text based on its content. NLP can perform tasks like language detection and sorting text into categories for different topics or goals. It can also sort by intent through a process known as sentiment analysis. NLP can determine the sentiment or opinion expressed in a text to categorize it as positive, negative, or neutral. This is useful for deriving insights from social media posts and customer feedback.
Natural language generation
Once a deep learning NLP program understands human language, the next step is to generate its own material. Using vocabulary, syntax rules, and part-of-speech tagging in its database, statistical NLP programs can generate human-like text-based or structured data, such as tables, databases, or spreadsheets.
Common natural language processing tasks
To understand how these NLP techniques translate into action, let's take a look at some real-world applications, many of which you've probably encountered yourself.
NLP is particularly useful in search engines like Google or Bing since there's no standard format for inputting a search request. Language processing helps search engines provide useful results by analyzing the meaning of user queries that may be as varied as "Where is there a good coffee shop near me?" "Coffee shops downtown New York" or "I need coffee now!"
Another common use for NLP is speech recognition that converts speech into text. NLP software is programmed to recognize spoken human language and then convert it into text for uses like voice-based interfaces to make technology more accessible and for automatic transcription of audio and video content. Smartphones have speech recognition options that allow people to dictate texts and messages just by speaking into the phone.
Virtual assistants, voice assistants, and smart speakers
It's become commonplace to say "Hey, Siri, find a nearby dry cleaner" or "Alexa, what's the weather?" Virtual assistants rely on natural language processing to understand what they're being asked, analyze what type of results the user really needs, and return that information in a way that's accurate and clear. Virtual assistants can use several different NLP tasks like named entity recognition and sentiment analysis to improve results.
Emails that end up in your spam folder are the result of another common NLP task that you probably appreciate. Many spam filters use NLP to find and block unwanted emails by identifying keywords and phrases that are commonly associated with spam and analyzing the links in an email to determine if they are malicious.
Autocorrect and predictive text
How does your phone know that if you start typing "Do you want to see a..." the next word is likely to be "movie"? It's because of statistical natural language processing, which uses language statistics to predict the next word in a sentence or phrase based on what is already written and what it has learned from studying huge amounts of text. It is also useful in understanding natural language input that may not be clear, such as handwriting.
The last time you had a customer service question, you may have started the conversation with a chatbot—a program designed to interact with a person in a realistic, conversational way. NLP enables chatbots to understand what a customer wants, extract relevant information from the message, and generate an appropriate response.
Many customers have the same questions about updating contact details, returning products, or finding information. Using a chatbot to understand questions and generate natural language responses is a way to help any customer with a simple question. The chatbot can answer directly or provide a link to the requested information, saving customer service representatives time to address more complex questions.
Many people are familiar with online translation programs like Google Translate, which uses natural language processing in a machine translation tool. NLP can translate automatically from one language to another, which can be useful for businesses with a global customer base or for organizations working in multilingual environments. NLP programs can detect source languages as well through pretrained models and statistical methods by looking at things like word and character frequency.
Text and video summaries
NLP is also useful as a tool to summarize long texts or videos. Natural language processing algorithms extract data from the source material and create a shorter, readable summary of the material that retains the important information.
NLP algorithms do this in several ways. They can pull out the most important sentences or phrases from the original text and combine them to form a summary, generating new text that summarizes the original content. They can also use resources like a transcript of a video to identify important words and phrases. Some NLP programs can even select important moments from videos to combine them into a video summary.
Many organizations have access to more documents and data than ever before. Sorting, searching for specific types of information, and synthesizing all that data is a huge job—one that computers can do more easily than humans once they're trained to recognize, understand, and categorize language.
Challenges of natural language processing
While NLP algorithms have made huge strides in the past few years, they're still not perfect. Computers operate best in a rule-based system, but language evolves and doesn't always follow strict rules. Understanding the limitations of machine learning when it comes to human language can help you decide when NLP might be useful and when the human touch will work best.
Human languages aren't always clear
For all of a language's rules about grammar and spelling, the way we use language still contains a lot of ambiguity. The intended meaning of words and sentences isn't always clear.
Depending on the context, mood, speaker, or audience, natural language processing still isn't as proficient as human beings at understanding subtle nuances like different word forms or which sounds constitute separate words, especially when someone is speaking quickly.
Here are some challenging examples that demonstrate why NLP isn’t always accurate:
- Slang: Slang is often changing, fast-evolving, and not always included in dictionaries or models used for language processing.
- Humor: Humor can depend on the tone of voice, timing, and other subtle cues that are difficult for AI to process. Certain types of humor—like puns—rely on words that have more than one figurative meaning.
- Errors: No one's speech or writing is completely perfect. As humans, we can usually understand the meaning behind human language even if there are errors in pronunciation, spelling, or word use. But natural language processing models are more rule based and are less able to adapt in the moment.
Human language is culture- and context-specific
Anyone who has studied a foreign language knows that it's not as simple as translating word-for-word. Understanding the ways different cultures use language and how context can change meaning is a challenge even for human learners. Automatic translation programs aren't as adept as humans at detecting subtle nuances of meaning or understanding when a text or speaker switches between multiple languages.
There is a lack of data for low-resource languages
In high-resource languages (languages for which there is a large amount of annotated data, such as English and Chinese), it's possible to train NLP models with high accuracy. However, in low-resource languages, there is often a shortage of annotated data (text data that has been labeled with relevant information, such as named entities, part of speech, and syntax.)
Annotated data is used to train NLP models, and the quality and quantity of the annotated data have a direct impact on the accuracy of the models. As a result, NLP models for low-resource languages often have lower accuracy compared to NLP models for high-resource languages.
Missing or inaccurate data skews results
Deep learning techniques rely on large amounts of data to train an algorithm. If data is insufficient, missing certain categories of information, or contains errors, the natural language learning will be inaccurate as well. However, language models are always improving as data is added, corrected, and refined.
How natural language processing can help your business
Natural language processing algorithms are used in almost every field. There are many ways that natural language processing can help you save time, reduce costs, and access more data. Read on to see which NLP tasks might be right for your business.
Perform analytical jobs
Many organizations find it necessary to evaluate large numbers of research papers, statistical data, and customer information. NLP programs can use statistical methods to analyze the written language in documents and present it in a way that makes it more useful for extracting relevant data or seeing patterns.
NLP is particularly useful for tasks that can be automated easily, like categorizing data, extracting specific details from that data, and summarizing long documents or articles. This can make it easier to quickly understand and process large amounts of information.
Monitor social media
NLP can analyze customer sentiment from text data, such as customer reviews and social media posts, which can provide valuable insights into customer satisfaction and brand reputation.
Keep an eye on competitors
Just like NLP can help you understand what your customers are saying without having to read large amounts of data yourself, it can do the same with social media posts and reviews of your competitors' products. You can use this information to learn what you're doing well compared to others and where you may have room for improvement.
Help with customer service
NLP can be used to automate customer service tasks, such as answering frequently asked questions, directing customers to relevant information, and resolving customer issues more efficiently. NLP-powered chatbots can provide real-time customer support and handle a large volume of customer interactions without the need for human intervention.
In addition, speech recognition programs can direct callers to the right person or department easily.
Elevate your writing
While natural language processing can't do your work for you, it is good at detecting errors through spelling, syntax, and grammatical analysis. You can use an NLP program like Grammarly or Wordtune to perform an analysis of your writing, catch errors, or suggest ways to make the text flow better.
Five natural language processing tools for you
If you're ready to put your natural language processing knowledge into practice, there are a lot of computer programs available and as they continue to use deep learning techniques to improve, they get more useful every day.
Some natural language processing applications require computer coding knowledge. Python is a computer programming language that is particularly popular for NLP tasks, but there are ways to make NLP work for you even if you're not a programmer or if your organization doesn't have a dedicated IT department that can work with Python code.
If you've decided that natural language processing could help your business, take a look at these NLP tools that can do everything from automated interpretation to analyzing thousands of customer records.
1. Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service offered by Amazon Web Services (AWS). It uses machine learning algorithms to analyze and understand text data as well as provide insights and information about the text. It can also perform tasks like redacting personal or sensitive information from text and documents.
2. IBM Watson
Originally developed to answer questions on the television quiz show Jeopardy, IBM's Watson NLP is more than just a cool bit of entertainment. It's a powerful program that can be customized and scaled for businesses that need to analyze, understand, or generate language. And if you want to know whether Watson knows what it's doing—it beat two Jeopardy champions and won $1 million!
3. Google Cloud
Google Cloud Natural Language Processing (NLP) is a collection of machine learning models and APIs. Google Cloud is particularly easy to use and has been trained on a large amount of data, although users can customize models as well. Google Cloud also charges users by request rather than through an overall fixed cost, so you only pay for the services you need.
4. Deep Talk
Deep Talk is designed specifically for businesses that want to understand their clients by analyzing customer data, communications, and even social media posts. It also integrates with common business software programs and works in several languages.
spaCy is an open-source Python library for advanced natural language processing. It was designed with a focus on practical, real-world applications, and uses pre-trained models for several languages, allowing you to start using NLP right away without having to train your own models.
Natural language processing can help your business automate tasks, improve customer service, and analyze large amounts of data—and you don't need a degree in computer science!