During the introduction of computer science, computers used to consume almost a whole room to save just 1MB of data. But today, history sounds as funny as a joke because, at present, we are capable of storing trillions GB of data in cloud storage.
Everything has changed a lot since the advancement of computer technology and data science. Almost 2.5 * 10 ¹⁸ bytes of data is generated every day. But we are still using the same old methods of data processing, i.e., text data mining. So, what else can we do? Do we have any other options?
What is NLP?
Natural Language Processing (NLP) is an AI-based program. It helps in creating a conjunctive and humanize interaction between humans and computers. It is designed for technologies to understand natural human language, and converting responses into natural human language.
Suppose you have an article about Chatbots. And you want your system to read it. Well, reading an article is a simple command-based task for any program, which is easy to execute. But what if you want your system to learn that article?.
“Chatbots are AI-Based conversational support, and marketing tool. They can be trained to perform many monotonous and instrumental tasks as per the requirements, such as booking an appointment, providing assistance, advertising products or services, and strengthening relationships with customers.”
Can your system answer “What is a chatbot?”, or “For What Chatbot Can Be Trained?”. Even though your system has enough data to answer these questions, it still won’t be able to answer you. You can’t say that your system is weak or unadvanced because it was not able to answer you. It happened because your system wasn’t able to even understand your question. And even if the system can understand the question, then it won’t be able to reply to you in your language. The necessity of NLP comes to the display at those times. With the presence of NLP in your system, you could have got the answers to the above questions instantly.
How Does NLP Work?
→ Human Gives Command To The System
→ NLP Translates The Command into System Language
→ NLP Extracts Or Predicts The Answer From The System Data
→ NLP Converts The Reply Into Human Language
→ NLP Delivers Reply To User In Human Language
Latest developments and future predictions
By now, you must have understood how NLP works, and why data is needed to increase the accuracy of results from NLP.
Amount of Relevant Data ↔ Accuracy of Results From NLP
But as you already know how much data we surf and create every day, you can presume the bright future of Higher Result Accuracy From NLP.
What if we can upload all the data that exist in human minds in a system and integrate NLP in it?
Doesn’t it sound exciting?.
Maybe it isn’t happening today, or perhaps NLP hasn’t reached 100% accuracy “as of today.” But with an adequate amount of data, you can get still reach close to it.
Types Of Natural Language Processing
There are mainly two types of Natural Language Process, i.e., Rule-Based NLP and Predictive NLP.
It is the initial and oldest approach to NLP. Programmers still use it, as Rule-Based NLP’s has been one of the most successful NLP. It uses a rule-based system in which they incline to focus on pattern-matching or analysis. The rule-based NLP’s are mainly coded into the system in the form of if-then-else statements. It includes manually designing sequences of words, or part-of-speech, or another way of representing words in a sentence, and match sequences with the text.
If you have a chatbot on your website, and you want to teach it how to reply to greetings like “Hello,” “Hii” “Heyy.” It indeed can be an easy task if your chatbot has NLP in it. You can teach your chatbot using the rule-based method about welcome greeting words and how to reply to them.
Chatbot: Hi (Analysis that “Hey” is a greeting message from its learning)
You can further teach your chatbot about nouns, so that, every time a user tells chatbot their name. The chatbot can automatically capitalize on their name’s first letter.
User: Heyy… I am rahul.. and can you help me in selling my my hyundai i20?
Automobile Chatbot: Hi Rahul. I can help you with selling your Hyundai i20 (Analysis “Rahul” and “Hyundai” are nouns, so it capitalized them in the reply)
Predictive NLP is a more advanced approach than rule-based NLP. It works on the concept of learning from observations and recurring examples. The accuracy rate is higher in this method than rule-based NLP. This approach is entirely based on machine learning algorithms to understand languages without being explicitly programmed. It can identify patterns from large amounts of data and set its own lingual rules. Empowers it to analyze possible input and the most suitable lingual output.
Suppose that a user is conversing with a banking chatbot named “Lisa”:
User: Hi Lisa. I am Rahul.
Lisa: Hi Rahul. How may I help you?
User: Lisa, can you show me my last five transactions?
Lisa: Sure Rahul. Here are the last five transactions from your account.
User: Can you repeat the last transaction with Mr. Rakesh again?
Lisa: Sure. Do you want to change the amount?
User: No, keep the amount same. And can you also text him to notify about the transaction?
Lisa: Sure. Amount transfer initiated. I will text Mr. Rakesh to notify him about the transaction.
In the context of the above example, you can see how Lisa analyzed him as Mr. Rakesh without being told. It is termed as relation extraction. It is a task performed by predictive NLP to identify relations between pairs of named entities from conversational flows or just free text.
NLP for Cyber Security Purposes
It is quite a wandering development voyage of communicating with a computer using programming languages to natural conversations with tools like chatbot and virtual assistants. There have been few risks also generated in the journey regarding cybersecurity.
The concept of NLP is not just limited to the humanizing bonding of humans and computers. NLP can serve other useful purposes also in the field of Cyber Security.
NLP can successfully overcome fiscal and social cyber risks if integrated with chatbots or virtual assistants with its broad range of features like language modeling, sentiment analysis, text classification.
It also has other feature to face cyber risks like relation extraction, named-entity recognition, natural language inference, semantic parsing, co-reference resolution, entity linking, relational reasoning, semantic composition, language identification and translation, entity and information retrieval, intent detection and classification, stance and fake news detection, rumor detection, hate speech detection, clickbait detection, abuse detection.
How Can Chatbots With NLP Help Businesses?
Chatbot itself is a beneficial marketing and business empowering tool. And “NLP integrated chatbot” is an unpredictably remunerative idea. It can function more accurately and provide results, even more, profiting in terms of ROI.
The main target and purpose of a chatbot are to make better interaction with its users for generating higher leads. Natural Human Language understanding of NLP can help chatbots to achieve its goal more accurately with its higher analytical and prediction ability.
Emotional Analysis Ability
One of the most important factors that a chatbot lack is its ability to understand human emotions. Although continuous training may enable chatbot to become better in terms of emotional understanding too. But with NLP integrated into the chatbot, it can itself learn and become better itself. And that too more accurately than manual training.
Prediction For Higher Upsell
Businesses don’t just expect to get a higher sale of their product. Upsell is an equally needful prospect for them. A chatbot is capable of making predictions based on previous purchases of the customer. But with NLP integrated into it, it could make use of emotional and lingual analysis of NLP to give even more accurate probable outcomes.
User Researching Ability To Deliver Better Customer Satisfaction
“Consumers don’t think how they feel. They don’t say what they think, and that’s the biggest challenge for businesses”
Somehow, this quote ultimately relates to how sometimes marketing becomes challenging for businesses, as they are also mostly unable to understand consumers’ psychology towards any product. Although it is typically and humanly impossible as well to make it understandable. But NLP integrated chatbot has shown success in overcoming that issue too. There are many famous examples of how accurately AI-based chatbot made the probability of customer possible purchase with researches based on other users’ purchase preferences.