Continuous inventions in different fields of Artificial Intelligence leads to delivering excellent results for every cause. Using the concepts of deep learning, face recognition, self-driven cars, and more concepts came nearly to be implemented while natural language processing (NLP) remained a few steps behind. However, the increasing demands for smarter solutions have forced professionals to start again with natural language processing, and the progress achieved is tremendous.
Author: Gaurav Kanabar, Founder and CEO of Alphanso Tech
Natural Language Processing: Connects Computers and Humans
As artificial intelligence aims to build autonomous digital systems, natural language processing is a part where human interaction with machines is given higher priority. Natural language processing (NPL) focuses on the interaction between humans and computers using a natural language. The aim is to make the computer read, learn, understand, and decipher the language used among humans and perform operations accordingly.
It can be achieved by implementing the concepts of machine learning that can help a machine learn the languages used by humans by studying the data using complex machine learning algorithms to derive appropriate actions. The primary application of the knowledge earned by a machine through natural language processing is analyzing and processing the text and words to derive appropriate actions.
Natural Language Processing: How AI Helps to Deliver Results
Also called NLP, natural language processing helps in processing the input audio or text of a user by converting into text (if audio!) and later processing it to generate desired outcomes.
Text Classification
The importance of understanding the text by classifying it in different classes, e.g., noun, verb forms, gender, adverbs, and more, is necessary to understand the exact and complete meaning of the sentence. The use of enhanced machine learning algorithms and a vast dataset derived from the enormous amount of information can help the machine understand each. The text classification can be done accordingly to derive the complete meaning of it.
This implies the use of neural networks and concepts of deep learning that can automate the learning and understanding the process of a machine when a natural language input is provided. Text classification is a must to let the machine clearly understand what the user has entered. There are various stages and steps by which the complete classification of the text can be done.
Labeling of Texts
Deriving whether a named entity is living or not requires text labeling to be done. The practical application of text labeling in the business world can be said to identify spam emails. 45% of the total emails sent are spam. Moreover, it helps in learning whether a submitted article is subjective or objective. Processing larger chunks of information at a single time is possible with the enhanced deep learning concepts implemented, and it helps in labeling these chunks also.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Moreover, text labeling helps in understanding the questions raised. If a machine is asked several questions together, it must be able to differentiate between them and understand what a particular question is about. Therefore, using natural language processing, the computer can be enabled to perform all these tasks automatically and efficiently to achieve the desired results from the program.
Sentiment Analysis
Based on the sentiments expressed in audio or text, the appropriate reply should be generated. Even for humans, this task is challenging. By just reading out a text or listening to an audio, the machine should be able to process it and derive the tone used, and categorize it accordingly. Using a sentiment analysis model, a text can be checked for polarity, whether positive or negative.
By connecting words and sentences to form a relationship and particular structure to find out whether they are positive or negative, the machine can efficiently help in filtering out the comments and opinions. The application of this particular module is most beneficial for businesses as it can help classify the product reviews and customer opinions between positive, neutral, and negative.
Research suggests that 90% of customers who read online reviews were influenced to buy products by reading positive reviews, while 86% of their buying decisions were influenced by negative reviews.
It becomes difficult for the program to classify between extremely positive, positive, neutral, negative, extremely negative reviews, but the training set is kept big and knowledgeable enough; this kind of classification can also be done. For example, a business is providing music streaming services to its customers through the Spotify clone. It becomes necessary for them to know the user feedback and understand the requirements. With the use of sentiment analysis, they can efficiently differentiate between positive and negative feedback and efficiently deliver the best services following the user requirements.
Answering User Queries
Machine learning helps computers to develop excellent skills, and this set of skills includes answering skills also. The practical example of this is chatbots. Using the chatbots, the user queries can be answered efficiently without adding any human touch to it. Running a business is incredibly difficult when you are supposed to answer all user queries at any time of the day. Providing 24×7 customer support is not easier. However, chatbots are here to save on the expenses and answer all questions efficiently without tiring out throughout the day.
Natural language processing enables the understanding of input text and mapping the keywords from the question asked to the set of answers stored; an appropriate response is generated. Moreover, with continuous learning programs of today, the enhancement of stored answers and analysis of user queries is done to improve the productivity and outcomes of the chatbots. And as a result, 27% of all chatbots resolve the entire chat by themselves. Moreover, 70% of millennials have reported a positive chatbot experience. Isn’t it great?
Language Translation
Businesses wanting to expand their business reach across the globe must be ready to meet the demands of customers coming from any remote corner. The only barrier between these customers and the business is the language. If a business is not able to understand what the customer is trying to convey, the possibilities of failing at meeting all the demands increases, and it ultimately affects the business reputation. But worry not as natural language processing is here to help out.
Using the complex models built specifically for efficient translation of customer queries and doubts, the businesses can understand what their customers want to say. According to a report by CBInsights, 8% of startups fail because they get stuck in legal queries, while 14% fail because of not meeting their user demands. The importance of fulfilling customer requirements is higher to save a business from legal problems or failure.
And if a business is serving customers all across the world, it becomes essential to understand and voice out the right answers for every question raised, no matter the language used. With language translation facilities by natural language processing models, a business can engage more customers and ensure business growth via excellent customer support delivered without the barrier of language in between as 84% of customers state that the experience provided by a company is as important as the product and service qualities.
Is There More?
Upcoming years will bring much more change in the technology trends. The importance of Artificial Intelligence in everyday lives will increase as the solutions will become more practical and user-centric. Hence, natural language processing will thrive in the times when text-to-speech and natural language understanding in machines will increase. And the future is promising much more than that. Applying the concepts of NLP in business, businesses can also benefit from providing an excellent user experience whenever they approach the business.
Author Bio:
Gaurav Kanabar is the Founder and CEO of Alphanso Tech, an India based IT Consulting company that provides Event Management Software development service and other app development services to individuals as per their specified demand. Besides this, the founder also loves to deliver excellent niche helping readers to have deep insight into the topic.
Twitter | Linkedin
Continuous inventions in different fields of Artificial Intelligence leads to delivering excellent results for every cause. Using the concepts of deep learning, face recognition, self-driven cars, and more concepts came nearly to be implemented while natural language processing (NLP) remained a few steps behind. However, the increasing demands for smarter solutions have forced professionals to start again with natural language processing, and the progress achieved is tremendous.
Author: Gaurav Kanabar, Founder and CEO of Alphanso Tech
As artificial intelligence aims to build autonomous digital systems, natural language processing is a part where human interaction with machines is given higher priority. Natural language processing (NPL) focuses on the interaction between humans and computers using a natural language. The aim is to make the computer read, learn, understand, and decipher the language used among humans and perform operations accordingly.
It can be achieved by implementing the concepts of machine learning that can help a machine learn the languages used by humans by studying the data using complex machine learning algorithms to derive appropriate actions. The primary application of the knowledge earned by a machine through natural language processing is analyzing and processing the text and words to derive appropriate actions.
Natural Language Processing: How AI Helps to Deliver Results
Also called NLP, natural language processing helps in processing the input audio or text of a user by converting into text (if audio!) and later processing it to generate desired outcomes.
Text Classification
The importance of understanding the text by classifying it in different classes, e.g., noun, verb forms, gender, adverbs, and more, is necessary to understand the exact and complete meaning of the sentence. The use of enhanced machine learning algorithms and a vast dataset derived from the enormous amount of information can help the machine understand each. The text classification can be done accordingly to derive the complete meaning of it.
This implies the use of neural networks and concepts of deep learning that can automate the learning and understanding the process of a machine when a natural language input is provided. Text classification is a must to let the machine clearly understand what the user has entered. There are various stages and steps by which the complete classification of the text can be done.
Labeling of Texts
Deriving whether a named entity is living or not requires text labeling to be done. The practical application of text labeling in the business world can be said to identify spam emails. 45% of the total emails sent are spam. Moreover, it helps in learning whether a submitted article is subjective or objective. Processing larger chunks of information at a single time is possible with the enhanced deep learning concepts implemented, and it helps in labeling these chunks also.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Moreover, text labeling helps in understanding the questions raised. If a machine is asked several questions together, it must be able to differentiate between them and understand what a particular question is about. Therefore, using natural language processing, the computer can be enabled to perform all these tasks automatically and efficiently to achieve the desired results from the program.
Sentiment Analysis
Based on the sentiments expressed in audio or text, the appropriate reply should be generated. Even for humans, this task is challenging. By just reading out a text or listening to an audio, the machine should be able to process it and derive the tone used, and categorize it accordingly. Using a sentiment analysis model, a text can be checked for polarity, whether positive or negative.
By connecting words and sentences to form a relationship and particular structure to find out whether they are positive or negative, the machine can efficiently help in filtering out the comments and opinions. The application of this particular module is most beneficial for businesses as it can help classify the product reviews and customer opinions between positive, neutral, and negative.
Research suggests that 90% of customers who read online reviews were influenced to buy products by reading positive reviews, while 86% of their buying decisions were influenced by negative reviews.
It becomes difficult for the program to classify between extremely positive, positive, neutral, negative, extremely negative reviews, but the training set is kept big and knowledgeable enough; this kind of classification can also be done. For example, a business is providing music streaming services to its customers through the Spotify clone. It becomes necessary for them to know the user feedback and understand the requirements. With the use of sentiment analysis, they can efficiently differentiate between positive and negative feedback and efficiently deliver the best services following the user requirements.
Answering User Queries
Machine learning helps computers to develop excellent skills, and this set of skills includes answering skills also. The practical example of this is chatbots. Using the chatbots, the user queries can be answered efficiently without adding any human touch to it. Running a business is incredibly difficult when you are supposed to answer all user queries at any time of the day. Providing 24×7 customer support is not easier. However, chatbots are here to save on the expenses and answer all questions efficiently without tiring out throughout the day.
Natural language processing enables the understanding of input text and mapping the keywords from the question asked to the set of answers stored; an appropriate response is generated. Moreover, with continuous learning programs of today, the enhancement of stored answers and analysis of user queries is done to improve the productivity and outcomes of the chatbots. And as a result, 27% of all chatbots resolve the entire chat by themselves. Moreover, 70% of millennials have reported a positive chatbot experience. Isn’t it great?
Language Translation
Businesses wanting to expand their business reach across the globe must be ready to meet the demands of customers coming from any remote corner. The only barrier between these customers and the business is the language. If a business is not able to understand what the customer is trying to convey, the possibilities of failing at meeting all the demands increases, and it ultimately affects the business reputation. But worry not as natural language processing is here to help out.
Using the complex models built specifically for efficient translation of customer queries and doubts, the businesses can understand what their customers want to say. According to a report by CBInsights, 8% of startups fail because they get stuck in legal queries, while 14% fail because of not meeting their user demands. The importance of fulfilling customer requirements is higher to save a business from legal problems or failure.
And if a business is serving customers all across the world, it becomes essential to understand and voice out the right answers for every question raised, no matter the language used. With language translation facilities by natural language processing models, a business can engage more customers and ensure business growth via excellent customer support delivered without the barrier of language in between as 84% of customers state that the experience provided by a company is as important as the product and service qualities.
Is There More?
Upcoming years will bring much more change in the technology trends. The importance of Artificial Intelligence in everyday lives will increase as the solutions will become more practical and user-centric. Hence, natural language processing will thrive in the times when text-to-speech and natural language understanding in machines will increase. And the future is promising much more than that. Applying the concepts of NLP in business, businesses can also benefit from providing an excellent user experience whenever they approach the business.
Author Bio:
Gaurav Kanabar is the Founder and CEO of Alphanso Tech, an India based IT Consulting company that provides Event Management Software development service and other app development services to individuals as per their specified demand. Besides this, the founder also loves to deliver excellent niche helping readers to have deep insight into the topic.
Twitter | Linkedin
Share this: