We speak what we were taught – English or any other regional language. But how do machines understand what we talk and process that information to give us the appropriate results? That’s where Natural language processing comes into picture.
Natural language processing is a part of artificial intelligence that helps computers/machines to analyze, process, manipulate and understand human languages.
You may ask if machines can process text, read them and what is the need for understanding them. Well the answer is simple. Imagine, you are asking your virtual assistant like Siri or Alexa or google assistant about ETA (Estimated time of arrival). Well without NLP, you have to use the sentence, “What is the Estimated Time of Arrival?”. Because machines read the letters ETA as it is and would not abbreviate it. What’s the point of using an assistant if giving a command is going to take a lot of time!
Any NLP program works in three parts.
Step one: Speech to Text
Step two: Part of Speech
Step three: Text to Speech
The step one of the NLP process is to understand the natural language received by the machine. With various speech recognition methods, natural language is converted into machine understandable programming language. Then the machine statistically (by comparing with previous speech), determines the sentence (words).
In step two, the words are identified based on their grammatical category like noun, pronoun, verb etc. and are tagged. By now computers or the machine understands what was the speech or instruction that humans gave as input.
In the final step computer programming language is converted to text or audible output and relevant results gathered are presented to the human.
These three steps happen in five parts.
They are lexical analysis, syntactic analysis, semantic analysis, disclosure integration and pragmatic analysis.
In lexical analysis, the words are identified, and the structure of words is analyzed. Lexicon in general means arrangement of words of a language in an alphabetical order according to their meaning and definitions. So, in lexical analysis, the speech is converted to text and is grouped by words.
Syntactic Analysis deals with analyzing the grammar of the sentence. Here the words are arranged grammatically to show the relationship between the words. Any sentence with wrong grammar might be rejected by the system.
In semantic analysis, the meaningfulness of the words is analyzed. Here the exact meaning of the text is obtained.
Discourse integration is all about correlation with previous or next sentence. Meaning of the sentence will change with respect to context, and that is why discourse integration is done.
In pragmatic analysis, the speech input is re-interpreted by the machine or system on what it meant actually and the result is displayed.
This one is a no-brainer. All the smart voice assistants like Siri and Alexa use NLP. They truly act like a personal assistant from making calls to setting reminders for us and have become an essential addition to the smart phones and smart homes. What more, now these smart assistants have evolved so much that they give witty comebacks and give more human-like interactions.
You might have noticed that google mail categorizes your emails to primary, promotions and social mails? Well, this is one of the use cases of NLP. Google Gmail uses NLP to read the words/keywords in the mail and categorizes them. Spam filters in all email providers are done in a similar way, using NLP.
A decade or two back, language translations were often messy. The literal translation of a sentence didn’t make any sense in many contexts. Also, grammatically the translated sentences used to be wrong. Now with NLP, we get grammatically correct translations, not only that NLP enables the machine to understand the emotion/sentiment under the text and gives appropriate translation for the same.
Chatbots & Automated Calls:
Automated messages that were restricted to just “sorry I don’t understand please choose from options above”, has now evolved to give human-like options and take intuitive & logical decisions for a customer problem, all thanks to NLP powered by deep learning. Every time when we make a customer service call, you might have heard a sentence – “This call may be recorded for training and auditing purposes.” The training data for NLP are actually these call recordings, that help machines understand the emotions of customers and how human’s (call center persons) handle each query. And now NLP actually enables computer generated voices to sound similar to a human voice.
Word Predictions/ Predictive Text:
Have you ever wondered how the text messages or WhatsApp messages, when you type the keyboard gives suggestions and most of the time it will be what you will exactly type. Well, autocorrect, autotype, autofill are all applications of NLP.
One of the very popular jokes that surrounds the internet is how google is like a wife, it never lets you finish a sentence. Google does this using NLP. All the most asked questions are displayed based on the few words you type, not only that it ranks those predictions in a high likely order. And if you type a hotel name it displays booking in the search page itself, if you type in a flight name its departure and arrival status are displayed in the search result with the help of NLP.
Data and Text Analysis:
The Internet is filled with data and with the advent of big data, there is a humongous amount of data that needs to be processed. NLP makes it easier for data and text analysis, categorizing them based on their sentiments, type, tone for processing easily. Keyword extraction, finding patterns in unstructured data becomes easier with NLP.
There is a lot more about NLP and if you are interested in learning how to use NLP for your practical applications like social monitoring tools etc., you should learn about various NLP techniques. And for that you can contact us, we at Peakin will ensure to give you a tailor-made training about Deep learning for NLP just for you!