wapt image 2694

How Natural Language Processing work is Explained: Use Cases and Benefits

What is Natural Language Processing?

Natural language processing (NLP) is a component of artificial intelligence which supports the computers to understand the human language.

Natural language processing in a layman’s term means the technique which is used in AI and machine learning to help the system to understand and respond to the human language like a real human would do.

With a combination of computational linguistics and the field of computer science, this technology is brought into place.

This is achieved in order to close the gap between the processing of computers and human communication.


Use Cases of NLP

Think of the natural language processing in google search engine for example, you make voice search by speaking to your phone and the related answers are provided.

You only experience some symptoms but when you type how you experience it, google is able to interpret your query and tell you the actual illness or disorder.

Think of the article spinners and Google Translate app that you use to translate articles from one language to the other.

What about chatbots in customer relation services. All these are examples and use cases of NLP.


Where did NLP starts from?

The need for the communication gap to be filled between the humans and the computers has led this field to grow very fast.

This was possible with the support of big data and the available algorithms.

While humans communicate with the help of several languages, these languages cannot be interpreted by computers because the systems only understand machine language.

Related POST :   How To Transfer Data (Files) From Android To IPhone Easily

The processing happens with the crunching of the several zeros and ones in the system.

This, of course, leads to some possible errors.

The algorithms in place now help the systems to communicate in human form (in the case of digital assistants) where particular algorithms are even served for each user when there is a repetition in a given task.

There are voice-activated devices which will listen to the command given by the user and execute the command within seconds.

This is possible with the help of natural language processing which is combined with machine learning.


How does Natural Language Processing works?

There are several techniques which are used for interpreting the human language.

This is done with the help of combining ML and algorithm techniques.

Since the applications of natural language processing seem to happen in varied fields, there is a need for a variety of approaches based out of natural language processing.

There are some basic tasks which are handled with the help of natural language processing like speech recognition, language recognition, tokenization etc.

The implication of natural language processing is seen primarily in the case of the voice-activated virtual assistants.

Other than that this practice is used in the processing of emails, where similar format in spam emails is analyzed and notified to the user.

In the case of the mobile phones, there is the voice-based notification for missed calls which is possible with natural language processing.

Related POST :   A to Z of Whatsapp Video Call and Settings

It is also used in the content categorization when a user tries to enter text in the search bar of a website.

In the case of Telecom, NLP can be used in billing the customers.

All these are made possible by breaking down the language into smaller bits. Then these bits are analyzed and understood after which the tasks are processed.


Natural Language Processing Application

NLP can be used to search and arrange a given content based on the index for a language based document.

This document can be sets of data present on a website too.

It will also notify in case there is any duplication of the data.

The meaning of the content can be interpreted by natural language processing to which the advanced analytical techniques can be added to even make a forecast of the data.

The basic extraction of the inputted data can be done in seconds with this technique.

There is also the possibility to do sentiment analysis with a specific set of data by combining it with opinion mining.

Commands from the user can be taken by the system and then it can be translated into the machine language after which the given command can be executed.

While the conversion of languages used by humans to machine languages is possible on one side, the translation between the several languages used by humans can also be done here.

There are Natural language processing use cases in healthcare industry, where patients’ health education can be improved, individual patient’s needs are understood and the best approach to improving quality health care.

Related POST :   Reasons Google Chrome is a Fav Browser for Gamers

Example of use cases of NLP in banking and finance is in data analytics and customer relations where it is used to improve customer’s experience.

Complaint of individual client in the bank can be analysed in order to provide personalized service as much as possible.

Natural language processing is already transforming the retail banking and the future trend holds much improvement.


The need for natural language processing

Since the data keeps on growing along with the complications of the several language diversities in humans, the natural language processing is needed to convert these languages and then interpret it accordingly.

Since most of this data is in the unstructured, these systems are needed to analyze the huge set of data and to process it efficiently.

While every language used by humans is different, there is still a lot of complications attached to these languages in terms of grammar and accents.

All it requires is a semantic understanding for analyzing and interpreting the data and to provide a structure to it especially in terms of speech recognition.

While there are several benefits of natural language processing, the main limitation here is the issue of quality and accuracy in machine translation.

However, this can be more enhanced in the future and the pros definitely outweigh the cons.