Once an obscure subset of artificial intelligence (AI) development, natural language processing (NLP) is now a vital part of modern life. Whether it’s digital voice assistants, translation apps, word processors, or even customer service calls, you probably encounter this technology in your everyday activities much more frequently than you presume.
Welcome to our brief guide to NLP! In the first post of this series, we explored what NLP is and how it came to be. In case you missed it, you can read it here. For our second and final entry, we’ll delve deeper into how NLP works.
Rule-Based & Statistical Modeling: Which Is Better?
As we discussed in our previous post, early NLP systems relied on rule-based models to get the job done. These models were typically hand-written and hand-coded. In turn, this made them quite rigid and difficult to adapt to different problems.
Statistical models leverage machine learning (ML) to analyze large datasets of real-world language use, which, in turn, allows them to interpret and understand language rules. In contrast to rule-based modeling, statistical modeling systems are much more fluid and adaptable to the linguistic variances you’d encounter in numerous human speech patterns.
For rule-based models to “learn,” their handwritten rules must be modified by hand. Not only is this process laborious and time-consuming, but it’s also error-prone. Statistical modeling employs pattern-based ML algorithms that can interpret language variances to a much higher degree. Since these algorithms learn from recurring patterns, they can learn to automatically focus on specific problem areas after multiple iterations.
This automated ability to adapt swiftly and precisely makes statistical models superior to rule-based models. Not only are statistical models better performers, but they are also less hassle to maintain. Whereas rule-based models can only improve by increasing the complexity of the handwritten rules, statistical models can become more accurate simply by increasing their data input.
How Does NLP Work?
If you’ve gotten this far, then you know that there are a variety of language-interpretation techniques that can be used in NLP. They usually fall into one of these three categories: ML algorithms, statistical modeling, and rule-based modeling. Usually, computer systems will utilize a combination of these approaches to optimize the processing of human language information.
The main purpose of NLP is to transform the value point of language input data into something greater. By breaking down enormous sets of human language data into smaller, more logical components, NLP allows computer systems to understand the nature of our linguistic choices and their meaning.
Machine translation of languages, text-to-speech or speech-to-text conversion, categorizing, summarizing, and identifying sentiment are all facets of NLP. But they all boil back down the overarching goal we just mentioned: Deriving more value from the original language input data.
The Importance of NLP
Human language is so diverse and complex that not even humans can comprehend all of it. NLP helps close this colossal gap by taking on the onus of understanding text or speech and interpreting it so we can make sense of its intent. Besides helping humans understand one another, NLP also helps computers analyze information faster.
Language-based data comprises a gargantuan amount of the information that humans have at their disposal. And all of the computing and intelligent systems in the world are worthless unless they can understand and communicate with humans. So why not use this language-based data to “teach” these machines?
Systems equipped with ML algorithms can identify patterns in our language data that we cannot. By training them repeatedly on language data, these machines can analyze data with unprecedented speed and efficiency. This is not only great — it’s a necessity to keep up with the breakneck pace of tech growth.
More devices are being introduced into society with each day that passes. When you consider that humans have hundreds of thousands of languages, each with various regional dialects, it’s easy to see why NLP is necessary; no other technology can make sense of this unstructured data and connect the entire world to modern technology.
Currently, unstructured data presents one of the most difficult challenges for NLP. Whether it’s slang, made-up words, emojis, abbreviations, or lack of punctuation, these variances can still trip up NLP systems. But NLP’s ability to interpret these modifications and new additions to lexicons has become much better than it was before. Yes, there’s still a long way to go. But by adding structure to this unstructured data, NLP removes ambiguity from the equation so that interactions between man and machine are more positive.
NLP Is All Around You
Now that you know more about NLP, identifying real-world use cases of this technology will probably be much easier for you. Try taking a pause in your day-to-day to see what you can find!
Perhaps the most popular example is a voice assistant like Alexa, the smart home device from Seattle developer Amazon. Whenever you ask your voice assistant to check the weather or look up something else for you, NLP is making this interaction possible.
Another popular example of NLP is email filtering. That’s right — without NLP, your email inbox would be filled with tons of spam! Thankfully, this use of NLP arrived quite early compared to some of the other examples we’ve discussed, most likely because AI developers are just as sick of junk mail as we are.
Other use cases of NLP include phone-to-text transcription, language translation, grammar check capabilities of word processors, automated customer service calls… you get the picture. NLP is all around us. And this won’t change anytime in the near future.
We hope you’ve enjoyed this brief guide to natural language processing! The technology can be intimidating to learn about. But if you’re still processing this information, don’t fret; you can always reference this guide whenever you need to.
What’s your favorite NLP application? How do you think this technology will affect our future? Let us know your thoughts in the comments below!Tags: AI, AI and machine learning, AI and ML, AI App Developer, AI app developer Seattle, AI app developers Seattle, AI app development Seattle, app developers Seattle, benefits of machine learning, machine learning app developers, machine learning app development, machine learning applications, machine learning apps, machine learning developers Seattle, mobile app developers Seattle, mobile app development Seattle, natural language processing, NLP