AI Wakforce

Color logo with background
picture of words showing NLP or natural language processing

Share This Article

Machine Learning for NLP: What Is It and Its Applications?

6/23/2022 | 4 Min Read

picture with the words Natural language processing

If you aren’t new to the artificial intelligence field, you must have heard a thing or two about machine learning. You might even have heard of natural language processing, which most people call NLP.

But what do they mean, and how are they related? Are they any different from each other? Machine learning is how computer systems learn to think like humans.

NLP refers to how a computer system or program understands and decodes both written and spoken human language. That’s where the name natural language processing comes from.

How Does Machine Learning for NLP Work?

Machine learning is the process of teaching machines to be intelligent and remember everything they learned in the past through experience. Take the example of spam mail. Machine learning by classifying past spam messages knows them when they reappear in the future and categorize them.

This ability to make computer systems remember the things from the past enables machine learning to work for natural language processing. Everyone knows computers only understand 1’s and 0’s- binary language.

So how do they learn to understand other languages say English, Arabic, or Swedish? Enter natural language processing. Feeding a computer different languages will still have it process them as 0’s and 1’s.

But with machine learning, the computer will understand these different languages for what they are. That enables the computer to decode the words from human languages and thus execute given commands.

Here is how Machine Learning Helps Computers Understand human languages:

a) Pragmatic Analysis

How can you be sure whatever a computer translates into a human language makes sense? After all, the computer can only see things as a series of numbers! That’s where machine learning comes in with pragmatic analysis.

The pragmatic analysis allows a computer to detect deeper meanings from words. For instance, how do you think a machine detects sarcasm? Machine learning collaboration with natural language processes.

Few people understand how that is. Maybe we could put that down to the wonders of artificial intelligence.

b) Semantic Analysis

Some words have different meanings in different contexts, especially when using them in a sentence. Machine learning enables computers to distinguish these differences and to understand the words’ meaning.

Take the example of the word bank. There is a river bank, a commercial bank, or even a blood bank? This presents a particularly intriguing case for a computer to understand.

Yet it does by using Word disambiguation technology courtesy of machine learning. Machine learning eliminates the ambiguity, making it clear what the language means by feeding a machine those specific words in the different contexts. How outstanding is that!

c) Syntactic Analysis

Syntax is how words combine to form a coherent phrase according to the rules of a language. Machine learning enables NLP systems to understand the different speech parts and ensure the words follow the basic grammar laws for each language.

Is NLP different from ML?

Natural language processing and machine learning are all subsets of artificial intelligence. But they aren’t the same. While NLP refers to how computer systems learn to understand human languages, machine learning is the ability of computers to learn to think like humans.

However, they can work collaboratively to ensure better results for each other. Machine learning is integral to natural language processing by making it easier for computers to understand different languages.

The Applications of Natural Language Processing

Natural language processing has a myriad of applications. Here are some of the most import

1. Voice Assistants

picture of a phone with the words Ok Google

Unless you just crawled from under a rock, you have heard of Apple’s Siri, Google Assistant, and Amazon’s Alexa.

The three are some of the starkest examples of natural language processing.

Siri, Alexa, and the likes can understand your human language when you give them an order or ask a question. One of the most famous commands is asking the assistant to play you a song on your phone.

They haven’t stopped on the phones alone, though. Today it’s common to have thermostats, watches, robotic vacuums, and cars work with a voice assistant.

Next time you use any voice assistant, know it’s natural language processing at work. That’s how the computer system in the phone, car, thermostat or robotic vacuum understood you.

2. Search Engine Results

Have you ever asked yourself how Google knows what you’re searching for when entering a weirdly unrelated word into the search result?

Natural language processing enables the search engine to relate whatever term you use to the desired search result.

That’s why sometimes you’ll see it say, “did you mean….”

3. Language Translation 

In the past, it was impossible to tell what someone was saying online if they wrote it in a different language. With NLP, however, it’s now possible to ask for a translation of a foreign language to your preferred language.

If someone posts something in English and you can’t read it because you don’t understand it, you can auto-translate that to a language you know.

The exact process applies to the famous Google Translate. That’s NLP at work

4. Autocorrect and Predictive Text

Many times, autocorrect has messed up something by correcting something we didn’t want it to. You’ll mostly hear people blast autocorrect for this. Fair enough. But what of the many times does autocorrect its best work?

Predictive text and autocorrect are other examples of natural language processes at work. Next time predictive text predicts something entirely at odds with what you wanted, remember it’s NLP at work.

5. Email Filtering

picture of hands holding a phone

I guess you didn’t know this, did you? One of the most basic uses of NLP is categorizing emails in their specific orders. When an email hits your mail inbox, it’ll either be under primary, social, or promotion categories.

Its natural language processing with artificial intelligence makes that possible. The AI filters the messages and knows the ideal category to classify them under.

6. Text Analytics

With text analysis, you can gather large amounts of data from meaningless texts on social media or elsewhere.

While an uphill task for any company to do themselves, an NLP system offers a way to do precisely that.

NLP systems scour conversations on the net for brand mentions, making it massively easier to make sense of their unstructured data.

Such conversations are usually unstructured and thus complex to make much sense of. NLP structures them into understandable data sets by extracting keywords and other essential data.

Final Thoughts

Machine learning (ML) and natural language processing (NLP) are both branches of artificial intelligence. But they have an interdependent relationship.

Machine learning enables natural language processing to work better and faster, even if they aren’t the same.

I hope you now have a clearer idea of the relationship between the two and how they work together to make our lives more comfortable.

Wondering how we can support your business by providing high-quality NLP annotation services?
Explore our NLP Annotation Services for AI-Driven Machine Learning

AI Wakforce can solve all your Natural Language Processing data Annotations needs.

Subscribe To Our Newsletter

Get updates and learn from the best

RELATED RESOURCES

Model picture of an AI model with vehicles
Computer Vision

Training Data and Its Use in Machine Learning

Training data or a training dataset is the initial data used to train a machine learning or artificial intelligence model. Machine learning algorithms learn to

Wondering how we can support your business?

Explore our core industrial use cases