How so? If you rely on Siri, Alexa, Cortana, or Google to play your music, conduct a search, or shop online, then you definitely are among its millions of users. That’s because NPL is what makes it possible for those virtual assistants to understand what you want.
Reducing NPL to virtual assistants would be limiting its wide spectrum of uses, though. From predictive text features to phone-based assistants, Ruby developers and programmers are coming up with a lot of interesting ways of using this particular form of interaction. But let’s not get ahead of ourselves. First, it’s important to understand what NPL is and how it works before truly comprehending its potential.
What is Natural Language Processing?
As its name implies, it’s the technology that lets computers and devices understand how we talk. “Natural language” is crucial here. Traditionally, computers understood a series of commands and actions given in a strict form. Those commands were programmed by software creators when developing the product.
Thus, it took for the user to employ a specific input to obtain a result. If you wanted to search for something on your computer, you needed to click on the search icon. If you wanted to Google an actor’s age, you needed to input the name and the word “age” in your search. You get the picture.
Today, thanks to the numerous advancements in artificial intelligence (AI), a technology like NLP is possible. Through, you can talk to a computer or to your smartphone in the same way you talk to a friend and the device will probably understand you. So, instead of clicking the search button or inputting “X age”, you only need to speak up as if you were asking another person.
That might seem like no big deal but it actually is. Think about how you talk on a regular conversation. We use different words, bend our syntaxis, are vague, or even misuse our vocabulary. For a digital platform to understand all that, it takes for a highly sophisticated algorithm created through Ruby development or another suitable programming language.
That algorithm is responsible for the “processing” part of the definition. It takes the input in the form of human language, reads, identifies, understands, and generates a response from it. Since this technology is part of the AI subset known as machine learning, any NLP-powered software gets better at understanding what you mean the more you use it.
Now let’s see how this is possible
How does NLP work?
The interaction in which NLP takes part is fairly straightforward. Usually, you open the app or software that uses this technology and ask something from it. Maybe you want to search for something online or want to listen to music. It doesn’t matter. The device “hears” your request and converts the audio to text. Then, the algorithm processes said text to find out the meaning of what you just said. Once that’s done, the algorithm offers the appropriate response: either it reads the search results aloud, provides you with the info you wanted, plays the music, etc.
Naturally, all of those steps, that fit so neatly in just one paragraph take a lot of time and effort from Ruby development services to make them happen. On a conceptual level, the algorithm applies language rules to the input and later converts its findings to data it can easily manage. This is done by extracting meaning from every sentence and then combining all of them to grasp the overall meaning of the original request.
That, in turn, is possible thanks to syntactic and semantic analysis that tackle the tasks needed to understand what you are saying. Here are some of the techniques used in both analysis:
- Syntactic analysis: this is the evaluation of how words are organized in a sentence to make grammatical sense. So, for NLP to understand you, the algorithm needs to understand and apply basic grammar rules. This can be done using dividing words into individual units called morphemes, identifying individual words and what kind of words they are, and defining sentences from a block of text.
- Semantic analysis: this analysis covers the meaning of a text and it’s the hardest challenge when working with NLP algorithms. That’s because understanding the syntactic part might be easier but stemming meaning from them and interpreting their combination is a very tricky thing to do. Though we aren’t enjoying flawless NLP algorithms today, we have techniques like the Named Entity Recognition (NER) that helps us categorize parts of a text into predefined groups (such as names, events, places, common expressions, etc.). The algorithm also uses techniques to understand a word meaning through context and derive intentions from databases.
Of course, those analyses are applied on multiple occasions and their results are refined interaction after interaction. Every time you use NLP with your device, you’re teaching it something new. Popular products like Siri or Alexa uses the data of their millions of users to improve their workings for all of the devices, not just for individual users.
Uses and challenges of NLP
As mentioned above, Natural Language Processing is essential for virtual and digital assistants. But that’s far from being its only use. You can find this technology in several platforms, including:
- Phone assistants: also called Interactive Voice Response (IVR), these applications are used in the support service field to filter out certain kinds of requests, thus avoiding the burdening of call center representatives with the menial requests that can be easily managed by AI.
- Translation platforms: there are digital tools that can hear you talk and offer an almost instant translation. Google Translate offers such a feature, which uses NLP to offer more precise results.
- Word processors: you’ve surely noticed that whenever you write something in Microsoft Word, the editor underlines potential issues, including grammatical, syntactic, and semantical mistakes. This is possible thanks to the inclusion of NLP algorithms that know how we use the language and lookup for violations of their rules whenever you write.
- Predictive text: not so long ago, Gmail introduced a feature that tries to predict what you’ll be writing next to offer you quick suggestions. The idea is to save you time by avoiding you type the whole thing, using machine learning and NLP as the base mechanisms.
These are some of the current uses of NLP. And if you have ever used some of those tools, you’ll surely know what NLP’s biggest challenge is – understanding natural language completely. It takes years for a human to understand our language’s complexities and abstractions and even then, we never truly finish our language education.
That’s because all natural languages (how we talk and write) are constantly changing and introducing changes. Factors like tone can greatly differentiate a sincere affirmation from a sarcastic remark. Regional variations, ambiguous uses, and other things make it even harder for NLP algorithms to understand completely and unequivocally our messages.
How to teach all of those subtleties to a machine (or, what’s even better, teaching how to detect them) is the biggest challenge for NLP today. Developers are trying to overcome it by employing the user’s feedback, therefore they need to inform what they meant whenever something doesn’t truly work out.
Researchers keep working and collaborating with enthusiasts, programmers, and even Ruby development outsourcing teams to create smarter tools that are fully able to understand how we talk. Though the work is still going on, the results are there for all to see – we’re seeing more robust uses of NLP with each passing day, so it’s only natural that more breakthroughs are on the way.