We give an introduction to the field of natural language processing, explore how NLP is all around us, and discover why it’s a skill you should start learning.
Technology is embedded in just about every area of our lives. We rely on it to navigate the world around us and communicate with others. Yet until recently, we’ve had to rely on purely text-based inputs and commands to interact with technology. Now, natural language processing is changing the way we talk with machines, as well as how they answer.
Here, we take a closer look at what natural language processing means, how it’s implemented, and how you can start learning some of the skills and knowledge you’ll need to work with this technology.
What is natural language processing?
Let’s start with a definition of natural language processing. On a very basic level, NLP (as it’s also known) is a field of computer science that focuses on creating computers and software that understands human speech and language.
As we explored in our post on what different programming languages are used for, the languages of humans and computers are very different, and programming languages exist as intermediaries between the two.
Yet the way we speak and write is very nuanced and often ambiguous, while computers are entirely logic-based, following the instructions they’re programmed to execute. This difference means that, traditionally, it’s hard for computers to understand human language. Natural language processing aims to improve the way computers understand human text and speech.
NLP uses artificial intelligence and machine learning, along with computational linguistics, to process text and voice data, derive meaning, figure out intent and sentiment, and form a response. As we’ll see, the applications of natural language processing are vast and numerous.
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Why is NLP an important field?
The concept of natural language processing dates back further than you might think. As far back as the 1950s, experts have been looking for ways to program computers to perform language processing. However, it’s only been with the increase in computing power and the development of machine learning that the field has seen dramatic progress.
When we think about the importance of NLP, it’s worth considering how human language is structured. As well as the vocabulary, syntax, and grammar that make written sentences, there is also the phonetics, tones, accents, and diction of spoken languages.
We convey meaning in many different ways, and the same word or phrase can have a totally different meaning depending on the context and intent of the speaker or writer. Essentially, language can be difficult even for humans to decode at times, so making machines understand us is quite a feat.
Yet with improvements in natural language processing, we can better interface with the technology that surrounds us. It helps to bring structure to something that is inherently unstructured, which can make for smarter software and even allow us to communicate better with other people.
Ultimately, NLP can help to produce better human-computer interactions, as well as provide detailed insights on intent and sentiment. These factors can benefit businesses, customers, and technology users.
How does natural language processing work?
Now that we know a bit about what natural language processing is, let’s delve into the basics of how it works. It’s worth noting that NLP is a fairly advanced application of AI, so we’ll be looking at a very top-level explanation of the software. For a more detailed introduction to natural language processing, check out our Applied Artificial Intelligence: Natural Language Processing course, part of our AI ExpertTrack.
The first thing to know about natural language processing is that there are several functions or tasks that make up the field. Depending on the solution needed, some or all of these may interact at once.
On a basic level, NLP software carries out three main processes:
- First, it breaks down the language (voice or text) into its pieces
- Next, it tries to understand how these pieces of information relate to each other
- Finally, it attempts to create meaning from these relationships.
There are, of course, far more steps involved in each of these processes. A great deal of linguistic knowledge is required, as well as programming, algorithms, and statistics.
Natural language processing techniques
Below, we’ve provided some further details on how natural language processing works in relation to certain tasks:
- Speech recognition. This is the process of turning spoken voice data into text data. Speech recognition software breaks down the voice into individual sounds, using algorithms to piece together meaning.
- Word sense disambiguation. In many languages, the same words can have different meanings depending on context. Word sense disambiguation (WSD) is the process of analysing language to distinguish between meanings. For example, ‘they finished second’ and ‘it was completed in the last second’.
- Coreference resolution. This process helps to determine if and when words in text or voice refer to the same entity. A good example is resolving which pronouns (he, she, they) refer to which person or object.
- Sentiment analysis. Language often has a lot of subtleties. NLP can be used to extract emotions and attitudes, whether positive or negative, from language.
NLP, AI and ML
Natural language processing is a branch of artificial intelligence (AI). It also uses elements of machine learning (ML) and data analytics. As we explore in our post on the difference between data analytics, AI and machine learning, although these are different fields, they do overlap.
Each area is driven by huge amounts of data, and the more that’s available, the better the results. Bringing structure to highly unstructured data is another hallmark. Similarly, each can be used to provide insights, highlight patterns, and identify trends, both current and future.
Natural language processing shares many of these attributes, as it’s built on the same principles. AI is a field focused on machines simulating human intelligence, while NLP focuses specifically on understanding human language. Both are built on machine learning – the use of algorithms to teach machines how to automate tasks and learn from experience.
If you want to learn the highly sought-after skill of machine learning, our AWS: Machine Learning Foundations microcredential is the ideal place to start.
Natural language processing examples
So far, we’ve focused on some of the general ways in which we can use natural language processing. Let’s turn our attention to some specifics. Below, we’ve highlighted some natural language processes use examples:
One of the challenges of NLP is to produce accurate translations from one language into another. It’s a fairly established field of machine learning and one that has seen significant strides forward in recent years.
Of course, there are many elements to consider here. A direct word-for-word translation often doesn’t make sense, and many language translators must identify an input language as well as determine an output one.
Older forms of language translation rely on what’s known as rule-based machine translation, where vast amounts of grammar rules and dictionaries for both languages are required. More recent methods rely on statistical machine translation, which uses data from existing translations to inform future ones.
As we explore in our open step on conversational interfaces, 1 in 5 homes across the UK contain a smart speaker, and interacting with these devices using our voices has become commonplace. Whether it’s through Siri, Alexa, Google Assistant or other similar technology, many of us use these NLP-powered devices.
Many of these smart assistants use NLP to match the user’s voice or text input to commands, providing a response based on the request. Usually, they do this by recording and examining the frequencies and soundwaves of your voice and breaking them down into small amounts of code. This code is then analysed by an algorithm to determine meaning.
If you want to learn more about how and why conversational interfaces have developed, check out our introductory course.
Search engine results
Search engines have been part of our lives for a relatively long time. However, traditionally, they’ve not been particularly useful for determining the context of what and how people search.
Semantic search, an area of natural language processing, can better understand the intent behind what people are searching (either by voice or text) and return more meaningful results based on it.
You’ve likely seen this application of natural language processing in several places. Whether it’s on your smartphone keyboard, search engine search bar, or when you’re writing an email, predictive text is fairly prominent.
This type of NLP looks at how individuals and groups of people use language and makes predictions about what word or phrase will appear next. The machine learning model will look at the probability of which word will appear next, and make a suggestion based on that.
Future applications of natural language processing
Natural language processing is a technology that many of us use every day without thinking about it. Yet as computing power increases and these systems become more advanced, the field will only progress.
Below, we’ve highlighted some of the ways in which NLP might advance in the future:
- More advanced assistants. Smart assistants will likely become smarter, being able to carry out more functions and communicate more naturally than they can currently. We may rely on them for more tasks in our daily lives.
- Greater integration. Natural language processing may become more ingrained in our lives. Both inside and outside of the home, we might find ourselves communicating with computers a lot more.
- More automation. We may see NLP automate more tasks. Chatbots and digital assistants could be used in more areas of customer service, answering queries, sending emails, and offering support.
- Smarter insights. Big data and sentiment analysis will give us a more detailed understanding of how people interact with technology, what they want from it, and how it can be better.
- Instant translations. Natural language processing could even allow us to instantly communicate with others, no matter whether we share a language with them or not.
Learning natural language processing
If you’re interested in getting started with natural language processing, there are several skills you’ll need to work on. Not only will you need to understand fields such as statistics and corpus linguistics, but you’ll also need to know how computer programming and algorithms work.
Below, we’ve picked out some of the main skills you’ll need to work in NLP:
Start with Python
We’ve already explored the many uses of Python programming, and NLP is a field that often draws on the language. What’s more, Python has an extensive library (Natural Language Toolkit, NLTK) which can be used for NLP.
Discover AI and machine learning
Next, you’ll want to learn some of the fundamentals of artificial intelligence and machine learning, two concepts that are at the heart of natural language processing.
Our ExpertTrack on deep learning and Python programming for AI will develop some of the knowledge you’ll need to pass the Microsoft Azure AI Engineer Associate Exam. For those interested in machine learning, our AI Design and Engineering ExpertTrack is the ideal place to start.
Specialise in NLP
Once you have a working knowledge of fields such as Python, AI and machine learning, you can turn your attention specifically to natural language processing.
Our course on Applied Artificial Intelligence looks specifically at NLP, examining natural language understanding, machine translation, semantics, and syntactic parsing, as well as natural language emulation and dialectal systems.
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Natural language processing is a fascinating field and one that already brings many benefits to our day-to-day lives. As the technology advances, we can expect to see further applications of NLP across many different industries.
Those interested in learning more about natural language processing have plenty of opportunities to learn the foundations of topics such as linguistics, statistics, Python, AI, and machine learning, all of which are valuable skills for the future.