APPLICATION OF NATURAL LANGUAGE PROCESSING (NLP) IN DEEP LEARNING
Natural language processing is a branch of deep learning which mainly deal with textual data. It understand textual and gives out conceptual meaning out of it. It is very difficult to make software through regular approach which can understand human natural language as we human do and take steps based on it understand but through deep learning (AI) you can make computer understand human natural language easily. Human language when written by human contains many information such as emotions, satisfaction, anger, facts, personal attitude understanding. All of above information is encoded in language we human can understand and interpret it. Due to deep learning approaches you can also make computer to understand and decode all information in person speech and act according due to that information. In this post we will discuss some of this applications and how deep learning learns all of this information from human language.
EMOTION DETECTION APPLICATION
When human speaks its language contains information about its topic to which he or she is speaking. He/she uses some extra words to express his/her emotions which we human can understand easily. When human is sad his/her emotions is expressed in human language , which in turn we hear that ,understand its emotions and react back to it in manner which satisfied speaker. Let say we have customer who is very angry at our product which comes and start shouting at us, we understand it anger through its sentences and respond back through appropriate answer. Likewise if we make some deep learning program which understand human's emotion and respond back to it with proper answer that can increase our customer satisfaction.
Likewise we can make chat bots whose responsibility it to talk to our customer such as customer will not able to identify that it is machine or human. It decrease our cost for customer executive and you can now make customer services 24x7 . Because bots don't need to take rest.
PERSONAL ATTITUDE IN LANGUAGE
Each human speech also inculcate its attitude in it which human judge easily. Likewise deep learning model can also judge and take action according. Let say you develop fraud detection system using NLP where our model jobs is to find fraudulent customer through its email. Our model will read its email and decide through its writing that this customer will pay us back or not.
Neural network of NLP are used to translate from any language to desire language. You can train model in any two language and that model will now able to translate for that two language or converting Audio to text also. We have to use NLP for text translation side . It is also called speech recognize system.
Through NLP ( Natural language processing) you can extract writing style of writer. Let say you have text of Shakespeare we feed it to our deep learning model. Our model will learn writing pattern of Shakespeare due to which that model recognize Shakespeare poem from unlabelled poem of many writers and can also generate new text in Shakespeare writing. This can be possible through next word prediction model.
Different companies use NLP in different forms. Facebook uses to understand post which is posted by its user to categorized and cater to new user according to its preference. Likewise Google use to understand website context to cater right website to user needed. Any company can use NLP in its basic functionality which will help company. You can use NLP in understanding and categorized customer feedback so customer care executive help customer efficiently. It can also use to read employ cv for your company, It can also use to read customer emails and message and judge which customer is highly unsatisfied and gone leave, so that you can take efficient step to retain that customer back. Anywhere human is required to understand text and reply to it that can be automated by NLP models.
Different approaches used to design NLP contains LSTM, GRU and RNN layers to learn patterns. Papers which revolutionized NLP are BERT, ULMFIT and transformers paper of openAI.
In this post we have seen application NLP ( natural language processing) in details. We have seen its understanding and replying power. If you have any doubt kindly put comment I will be happy to help you.