Have you ever thought how practically magical it is that your phone can anticipate what you’re going to say next? Or that you can ask ChatGPT to complete a wide range of prompts and it’s ready for the challenge? While it does seem like magic every once in a while, the truth is a bit different: it’s natural language processing, or NLP, that is making that happen behind the scenes. If you’re not an NLP expert yet, don’t worry: it’s a relatively new branch of computer science that has developed rapidly over recent years.
And this article is the perfect place for you to be. We’ll cover the basics of NLP, sharing everything you need to become an expert–including some of our tips and tracks to mastering NLP tools on your own.
What is Natural Language Processing?
As we mentioned above, natural language processing is a branch of computer science and artificial intelligence that has one main goal: reaching a point where computers can understand the spoken and written word in a human-like fashion. While this might sound easy enough on paper, consider some of the intricacies of human speech, such as slang, metaphors, irony, sarcasm, and tone of voice, not to mention dialects or accents.
Natural language processing and natural language generation (NLG) are frequently used together, in addition to natural language understanding (NLU). The difference is key: NLP understands the input, NLU processes the information and decides how to respond, NLG responds.
As you can imagine, this is quite the challenge. NLP brings together computational linguistics (the rule-based modeling of human language), statistics, machine learning, and deep learning models. And while we can teach computers to understand the conventional dictionary definitions of human language, the following are quite the challenge:
Words that change meaning depending on context: in all languages, not just English, there are words that change meaning depending on their context. Teaching a computer to understand what can be more than twenty different meanings and tones from just one word is difficult.
Errors: very few people (if any!) can speak perfectly, with no grammatical or pronunciation errors. Understanding slight variations is quite the ask.
Ambiguity: depending on tone of voice, body language, or word choice, a sentence could meet totally different things; humans would use these indicators to understand the true meaning, computers cannot.
Colloquialisms and slang: different regions and even people use the same word to mean different things and if you cross the globe, the language might technically be the same, but use similar words with opposite meanings.
Less spoken languages: computers learn by being fed and processing large quantities of data and even with languages such as English or Spanish that are widely spoken across the world, by both native and non-native speakers, there is still a need for more and more data to become more accurate. For languages with very few speakers, reaching NLP levels is almost impossible.
There are tons of examples of the challenges NLP faces, but we want to head into the good stuff. Let’s dive right into how scientists teach NLP to begin to understand human language.
How does natural language processing work?
You understand the challenges above and are probably thinking well, that’s it, then! There’s no way to teach a computer to truly understand human language. And for a while, that was the accepted thought. But just as translation and automated translations have drastically improved over recent years, scientists have found ways to teach computers to better understand human language:
Speech recognition: this converts voice data into text data and is needed for any tool that will receive spoken words as its commands or data, such as Google Home or Alexa. Computers undergoing speech recognition training will train on lots of different kinds of people: speakers who slur, others who mumble, others who use incorrect grammar.
Part of speech tagging: as we mentioned above, lots of words can have multiple meanings and even multiple parts of speech. Working with a computer to identify the part of speech of a word in a specific sentence helps differentiate between different uses.
Word sense disambiguation: feeding a computer lots of data with words in different settings with different contexts can help it differentiate between different meanings.
Named entity recognition: this helps computers understand when the name of a person or country is mentioned; of course, as new names become popular, this becomes increasingly challenging.
Sentiment analysis: possibly the most challenging for a computer, sentiment analysis tries to understand elements like attitudes, emotions, sarcasm, confusion, and anger from the text.
The five phases of natural language processing
To ensure that computers are learning the aforementioned manners of better understanding human language, these five kinds of analysis are the most commonly used in NLP innovation:
Lexical analysis: text or audio are separated into words and are analyzed, taking into account all of the complications we mentioned earlier.
Syntactic analysis: grammar rules are used to analyze the content as a whole and not individual words.
Semantic analysis: taking into consideration context, logical sentence structure, and grammar, semantic analysis defines the meaning of the sentence.
Discourse analysis: separate from semantic analysis, discourse analysis centers on the motivation behind a text, which demands a further and complex understanding of the text.
Pragmatic analysis: lastly, outside factors such as the time of the text or surrounding factors such as history and surroundings are used to provide an even deeper understanding of the text.
Applications of Natural Language Processing
We know that the idea of a computer that can understand the full range of human emotions seems a bit far-fetched–and as of right now, it is. But NLP is growing in popularity and many companies are investing a lot of time and resources into being the first to develop a truly revolutionary tool. And on the way to success, they’ve launched lots of cool things that surround us in our daily lives:
Spam detection: you’re thankful for your computer’s spam detection–it helps you keep that inbox clutter-free. But how does it work? Well, through NLP: it scans incoming emails for clues that it could be spam or a phishing attempt, using data from the past emails it was fed to determine what’s spam and what’s not. Common indicators of spam are misspellings, intense language, or grammatical errors: NLP is typically able to pick up on these.
Machine translation: you probably used Google Translate to get through your middle school Spanish classes and while it’s good for translating simple words, translation goes much farther than simply swapping out words with their counterpart in another language. For machines to be truly capable of quality translation, they’ll need to learn to understand the entire context to match the emotion, meaning, and outcome. Scientists and NLP are working to improve machine translation, but there’s still a long way to go.
Chatbots: when you head to a website and a chat pops up, or you want to reach a company’s customer service, chatbots are at work. These tools have been programmed with information about what clients are looking for, how to respond, and how to adapt their answers based on the client input. This technology has become commonplace over recent years and will continue to be so; the future of chatbots will involve them being able to answer questions outside of what they’ve been fed and respond even more accurately to human emotions.
Learning Natural Language Processing
You’ve seen the light–natural language processing is the future and it’s time for you to prioritize learning about NLP so that you’re ready to take on the future of tech. But how can you do this? Is it even possible? Of course it is! Just like with anything, there are loads of ways to learn it. But when it comes to NLP, there’s one important thing to keep in mind: it’s a relatively new subject, meaning there aren’t a ton of quality resources out there. If you’re serious about it, we recommend:
Familiarizing yourself with NLP online: the challenges we mentioned above are just some of the ones that scientists working with NLP face and you must be up for the challenge. Before you dive right in and buy a course to become the next NLP expert, explore job opportunities, career paths, and possible outcomes of becoming an NLP professional to ensure it’s the right choice for you. Check out research papers or publications from tech companies to make sure you’re reading quality information.
Take a course: from short YouTube courses to two month bootcamps, there are lots of ways to learn about NLPs. But before you make a decision, consider how you learn best: do you need structure? Do you benefit from lots of independence? If you want a structured course with a syllabus, a bootcamp may be the right choice for you; if you’re looking to have fun and just learn a little, online videos may be just what you need.
Check out some books: there’s truly no better way to learn a complicated subject like NLP than from reading theory. Because so much more goes into NLPs than other areas in tech, like a full understanding of the English language, starting your NLP journey with some expert theory can help you build a strong foundation.
Deciding to venture into a new area of tech and meet new needs head on is a brave choice and one that will definitely pay off in the future. But every new techie needs a solid education under their belt and Ironhack’s bootcamps are designed to provide just that: exactly what you need to land that first job in tech. Interested? We can’t wait to see you in class.
