Introduction to Natural Language Processing
The human language is complex and hard to understand for machines.
NLP is a concept that allows machines to understand human language effortlessly. In this blog post, let’s take a look at NLP and its working methodology in detail. We will also take a look at the use cases of NLP in detail.
Table of Contents
- What is Natural Language Processing?
- Example of NLP
- How does NLP work?
- Some more common techniques used in NLP
- Use cases of NLP in real-life
- Verdict
What is Natural Language Processing?
Natural Language Processing (NLP) is a combination of artificial intelligence with computer science. It allows you to create smooth interactions between computers and human languages. But isn’t that something we’ve been doing all this time?
Right now you are communicating with your device using human language. Whatever you type into the keyboard or whatever you click on the screen, will be converted into a series of 0’s and 1’s.
But NLP is focused on converting natural human language into machine-readable form. What do you mean by natural human language? Natural human language translates everything from the written word to the spoken language. For starters, the speech recognition application that you have on your smartphone is an extension of NLP.
Your smartphone uses NLP to convert your speech into machine-readable form. Once the machine understands your query, it processes it and gives you the corresponding output for it.
Example of NLP
Understanding NLP can be a bit hard if you’ve just started to learn about it. So we’ll give you a real-life example to understand NLP in detail.
Siri
If you own an iPhone or even know someone who has a product made from apple, you know who or what SIRI is. For others, SIRI is a voice assistant developed by the smart brains at Apple. You can use SIRI to do activities like performing a search on the internet, recognize a song, call someone, set a reminder and many more. You can use SIRI by calling out “Hey SIRI” or by tapping the home button on your smartphone. SIRI is a classic example of Natural Language Processing.
When you say “Hey SIRI”, you call out words from your natural language - English. In theory, this cannot be understood by machines. This is where NLP algorithms come into play by identifying the natural patterns in our language.
“Hey SIRI” is a pattern in your natural language. Your smartphone identifies it and activates the recorder. Now whatever you speak will be recorded and processed by NLP algorithms. It identifies patterns in your voice and creates a sentence from the words that you just said. Now, this is translated into 0’s and 1’s, before fed into your smartphone’s CPU. The CPU processes this request and instructs your smartphone to perform the requested task.
The best part is that SIRI allows you to do all these in seconds. For example, setting a reminder can be done in just a few seconds.
How does NLP work?
NLP puts forward certain algorithms that read your sentences and convert them into machine-readable form. But it’s not that simple since the algorithm has to take the tone and meaning of the sentence into account. It cannot just convert all the words into machine language individually. It needs to consider the context of those words in that particular sentence before deciding to convert them into machine-readable form.
For example, take a look at this sentence from a biblical copy - “The spirit is willing, but the flesh is weak”
In the early 1950s, a group of scientists used computers to translate this term into german. They succeeded at translating it to the German language. But when they tried to translate it back to English from German, something funny happened.
Here is what the computer returned
“The Vodka is good, but the meat is rotten”
The computer did not understand the context of that word in a certain position. So it used a similar word. But that did not fulfil the meaning of that sentence. To make sure that your NLP algorithm does not go through the same mess, you need to have a basic understanding of its working.
Here are 2 common techniques or working principles used in NLP processes:
- Syntactic Analysis
- Semantic Analysis
Syntactic analysis
The syntactic analysis helps the machine figure out how the natural language aligns with the grammatical rules in the language. This analysis will apply a set of grammatical rules to make sure that a group of words actually give some meaning together. Here are some of the most common techniques used in syntactic analysis
- Lemmatization
- Morphological segmentation
- Word segmentation
- Part-of-speech tagging
- Parsing
- Sentence breaking
- Stemming
Semantic Analysis
Semantic analysis conveys the meaning that is conveyed by a certain text. With that said, this is the most difficult form of analysis in natural language processing procedures. The semantic analysis helps you uncover the meaning and interpretation of individual words in the natural language.
Here are some of the most common techniques used in Semantic analysis:
- Named entity recognition
- Word sense disambiguation
Some more common techniques used in NLP
Parsing
Parsing is a simple concept used by computers to break down sentences into their constituents. This allows the computer to understand the relationship between each word in a syntactic form. After parsing, the computer can get a solid understanding of the type of sentence and its original meaning.
Stemming
Stemming is another process that reduces the words in a sentence to their stem. A stem is the part of a word that remains after all of its affixes are removed. For example, ‘Run’ is the stem word of ‘running’. ‘Touch’ is the stem of ‘touching’. Storing all the words in the English language with their stems can become tedious. That’s exactly why NLP algorithms perform stemming to just get the original word out of there.
Text Segmentation
Finally, text segmentation is another process that pieces it all together. It converts individual texts into meaningful sentences along with their intent. This process is present due to the complexity of human languages. For example, some people use the word ‘lifesize’ as it is, while others prefer to hyphenate it in terms of ‘Life-Size’. Text segmentation comes in really handy at times like these.
Use cases of NLP in real life
Sentiment analysis
A lot of organizations use NLP to identify what their customers are saying about their product or service. They scan keywords and identify common reasons or complaints with customers. With this, you can find which factors affect your customers and use that to build a better business.
Spam filtering
Organizations like Yahoo and Google employ NLP to prevent spam emails in their offerings. They identify certain spammy sentences, words and push them into the spam folder. If you use an email provider, go search for a folder named ‘spam’ and you will see a ton of emails in there's. Those emails have been marked as ‘spam’ by the NLP filters in your email provider.
Hiring purposes
Top performing organizations employ NLP as part of their hiring process. They use ATS to find keywords in the resumes of applicants. They also use NLP to identify potential candidates in the job market.
Verdict
Whether you know it or not, NLP has become a part of your everyday life. From the devices you use to the organizations that employ you, NLP is present everywhere. The age of ML and automation will only propel the use of NLP at breakneck speeds in the future!