Natural Language Processing (NLP): Definition & Examples

People usually find it easy to communicate with each other. We can easily talk with one another, derive meaning from text, identify and correct grammar mistakes, and resolve ambiguities and misunderstandings. When we try to transfer these capabilities to computer systems, it’s called natural language processing (NLP).

Natural language processing is a branch of artificial intelligence that allows computer systems to analyze and process human language. In doing so, AI technologies can perform natural language-related tasks like generating contextual output and providing experiences that feel conversational.

At the heart of popular AI-powered technologies like ChatGPT, Alexa, Siri, DALL-E, and Midjourney, you’ll find some form of natural language processing. It’s this functionality that enables these tools to process inputs that are in natural language, and to produce coherent responses—be it audio, text, or images.

Whether you’re a tech enthusiast, a business interested in automation, an AI hobbyist, or a student, understanding how natural language processing works can help you better integrate existing NLP tools in your workflows. In this article, we’ll explore what natural language processing is, how it works, and its applications in various fields.

What Is NLP?

Natural language processing is the technology that allows computer systems to analyze and process human language. For instance, when you ask Siri to play music, Siri will use NLP algorithms behind the scenes to determine what you want and then execute the command.

Through natural language processing, you can perform tasks like asking smartphones for directions or help in recognizing a song playing on the radio. You can also use generative AI tools like ChatGPT to produce long-form content based on specific topics, brand tone and voice, and target audience. Automated call centers also use natural language processing to handle customer inquiries and provide specific pre-programmed responses.

While a lot goes on behind the scenes, natural language processing typically works as follows:

1. First, a user submits a particular message or input.
2. The input is passed through a pre-processing stage, where it’s broken down into smaller units known as tokens. Common or filler words like "a," "the," and "to" are also filtered from the input, and words are also transformed further into their root form. For instance, "changing" can simply be converted to "change."
3. Once the input has been filtered in this way, it’s passed to the NLP algorithms. NLP-powered applications can either use rule-based or machine-learning algorithms.

Rule-based vs. machine-learning algorithms

Rule-based algorithms will use the processed input to scan for matching words and phrases in pre-programmed responses and then return a specific pre-meditated response.

Machine-learning algorithms attach to each token a certain weight and bias. These weights and biases determine the next node that the token will be passed to. At each node, a statistical algorithm is applied and the weights and biases adjusted. This pattern repeats until an output is derived.

NLP and big data

An important part of the Big Data revolution has been the rise in the use of unstructured data. Thanks in large part to systems like Hadoop and Spark, we now have the ability to quickly process huge troves of unstructured data that in the past would have just been left sitting in boxes and warehouses.

While many NLP tasks may not require the same kind of real-time streaming analytics as some other Big Data tasks, it does require facility working with large, unstructured datasets, whether in the form of text pulled from webpages, Facebook posts, search queries, text messages, or more.

Common NLP tasks and open-source tools

Some of the most common tasks for NLP include:

• Tokenization (splitting text into words and terms)
• Tagging various parts of speech
• Creating parse trees (which are like sentence diagrams)
• Classifying some terms as named entities (for example, grouping together names of people, days of the week, or cities)

Before we look at NLP’s more advanced applications, it’s worth noting that there are a number of open-source libraries that support both basic and more advanced NLP tasks. For example, Pattern and NLTK are written in Python and provide a number of classes and modules that make it easy to work with text.

NLTK is designed to be an intuitive, practical, and modular tool for NLP. It’s well documented, with two books and an active community in both academia and industry.

Pattern is billed as a web-mining module, and includes several tools that NLTK doesn’t, like a web crawler, HTML parser, and a number of APIs for major web services. Pattern also provides modules for graphic data structures that show the relationship between nodes representing different words or concepts.

Stanford CoreNLP is a Java-based suite of tools that provides similar functionality to NLTK. Described as an "integrated framework," CoreNLP is designed to make it easy to apply multiple tools to a single piece of text.

NLP examples and use cases

Natural Language Processing (NLP) combines linguistics, computational linguistics, and data science to enable machines to understand, interpret, and generate human language. Here are examples of NLP applications across various industries:

Customer service and experience

Finance and retail

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Health care and biomedical research

Marketing and content management

Information retrieval and processing

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Research and development

• NLP toolkits. Researchers and developers use various toolkits and libraries to build and experiment with NLP models.
• Embeddings. Word and sentence embeddings are crucial for many NLP tasks, representing linguistic items as vectors.
• Computational linguistics. This field combines linguistics and computer science to develop algorithms for processing and analyzing natural language.

These examples demonstrate how NLP, a key component of data science, is creating value across various sectors by enabling machines to process large amounts of unstructured text and understand human language.

Challenges and limitations

While useful, NLP isn’t perfect. The technology is constantly being improved, but machine learning algorithms apply a degree of uncertainty to tokens as they move between nodes in the system. On the one hand, this uncertainty helps the system generate unique responses. On the other, it can lead the system to generate outputs that are inappropriate or inaccurate.

Machine learning engineers, data scientists, and NLP specialists work to correct for these unwanted outputs by fine-tuning the machine-learning models. Nevertheless, these systems are imperfect and outputs should be verified for quality, accuracy, and tone.

Below are the primary challenges and limitations you may encounter with NLP:

• Language ambiguity. Natural language is inherently ambiguous, which creates challenges for NLP applications to process data accurately. For example, one word can have multiple meanings in different contexts, which computers will struggle to detect.
• Cultural nuances. How people speak varies from one culture to another. Even dialects of the same language can use different syntax, and some words and topics may be taboo. Especially for minority groups, NLP applications can struggle to identify and work with these cultural nuances.
• Bias. NLP models require vast amounts of data to train their algorithms. And they are only as good as their training data. If bias shows up in that training data—and isn’t corrected for by the machine learning engineers—then that bias will likely also show up in the model’s outputs.
• Insufficient training data. NLP applications also require large datasets of quality data for training purposes. Collecting and cleaning such data can be expensive and time-consuming.
• Multilingualism. While significant strides have been made, NLP tools still struggle with tasks based on languages other than English. This is because datasets in English are widely available, collecting and annotating data in other languages is resource intensive, and models trained in English can be later fine-tuned for other languages.
• Privacy. Like other AI applications, NLP tools can also collect confidential information and use it for training. There’s a chance of sensitive data leaking, leading to privacy concerns.

Future trends

The field of natural language processing is constantly changing. In future updates, computer systems will become even more effective at analyzing and processing natural language. Some future advancements in NLP could likely include:

• Better contextual understanding. In the future, NLP tools will process context in different situations more effectively. With this capability, they will generate more coherent and streamlined responses, facilitating more seamless conversational experiences.
• Zero-shot learning. NLP applications will continue leveraging machine learning algorithms to perform tasks even in areas where they haven’t been trained.
• Multimodal abilities. NLP capabilities will extend further to not only cover text but also images, audio, and videos. As a result, NLP tools will handle and generate different types of content, facilitating better human interactions.
• Multi-language support. As more data becomes accessible, NLP tools will also support different languages, reducing the impact of cultural nuances.
• Quantum computing. Leveraging quantum computing will accelerate the development of more efficient NLP and deep-learning language models. This technology will also enable NLP apps to quickly process complex and large datasets, uncovering deeper insights and hidden patterns and relationships between words.

How to get started with NLP

Natural language processing can help you understand the intricacies around the human language, including factors like semantics, pragmatics, and structure. With this knowledge, you can enable computer systems to process natural language and perform NLP tasks like machine translation, information retrieval, and content generation.

Want to master NLP? Consider enrolling in online courses to gain the necessary skills and knowledge. Platforms like Udemy and Udacity offer several NLP courses and tutorials that are entirely online.

As you continue to learn, join NLP communities on platforms like Reddit and LinkedIn, where you can connect with peers and mentors to enhance your learning experience. Also, engage in beginner projects like performing sentiment analysis on customer data and using NLP models to produce text to gain relevant hands-on experience. Take advantage of open-source tools like TensorFlow and NLTK to interact with NLP frameworks.

Having a background in computer science and familiarity with programming languages like Python can also enhance your mastery of NLP technology.

Find NLP help on Upwork

Natural language processing is a powerful technology capable of helping with tasks like language translation, information extraction, and speech processing. It’s applicable in numerous sectors, including customer service, finance, marketing, and e-commerce. By understanding NLP, you can create applications capable of processing user inputs and generating helpful responses.

However, NLP is a highly technical field that takes time to master, especially for beginners. Upwork can connect you with natural language processing freelancers to help you with NLP integration and personalized AI development as you learn NLP techniques and fundamentals.

And if you’re an NLP expert looking for work, start your search on Upwork. With many natural language processing jobs already posted, you can find projects to work on and grow your portfolio. Get started today!

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Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.