Introduction:

With the increasing use of artificial intelligence (AI) in chatbots and translators comes the use of NLP, or Natural Language Processing. NLP is a subfield of artificial intelligence that studies how human languages and computers interact. It is essential because it gives machines the ability to comprehend, interpret, and produce language similar to human languages. NLP has a plethora of applications in modern life, including AI chatbots, search engine recommendations, language translation, survey analytics, sentiment analysis, autocorrect, and email filters. 

 

How It Works:

NLP converts human language into a format that computers can comprehend and analyze. It begins with text preprocessing, which involves breaking the text into smaller units (tokenization) and standardizing it (normalization). Then, common words that don't add much meaning are removed. The text is transformed into numerical representations using methods that capture the meaning and context of words. These numerical representations are used by machine learning models for tasks like sentiment analysis, language translation, and text summarization, allowing computers to process and understand human language effectively.

 

Chatbots： 

In recent years, people have experienced a surge in the use of “AI chatbots.” With these new friends, one can converse, ask questions, check grammar, and even play riddles. These chatbots function by understanding natural human speech. NLP is seen everywhere. In daily life, one can notice ChatGPT, Google Gemini, Meta AI, and more. Many of our apps also have these “AI friends.” AI chatbots prove to be quite helpful, but they have also been topics of heated debates.

 

Applications in Business:

Natural Language Processing (NLP) is essential in understanding language speech and text fully. With NLP, differences in slang, dialect, and typos can be easily found and fixed. In businesses, NLP is used to effectively simplify and streamline operations. Companies use NLP in the process of going over sensitive data. Instead of looking over large volumes of sensitive data like medical records and personal information, companies use NLP to redact personally identifiable information and protect sensitive information. One example of an NLP system that helps companies with this data is Chisel AI, which helps insurance carriers extract policy numbers safely. Companies also use NLP to help with customer engagement. NLP technologies allow companies to converse with customers more easily. It allows the AI chatbots to sound and type in a more human-like way when real time staff members are not able to reach the customers. Thus, NLP helps companies keep their service up and operation costs low. One example of this is through T-mobile using an NLP system to give customers personalized recommendations by searching for keywords in chats. Additionally, companies can use NLP to help with business analytics. NLP helps companies gain a sense of how customers feel towards the company’s products or services. It does so by analyzing key phrases in customers’ feedback to determine the mood felt by them. One example of this is Success KPI, which helps contact centers derive actionable insights from call analytics.