Natural language processing (NLP) is the intersection of computer science, linguistics, and machine learning. The field focuses on communication between computers and humans in natural language and NLP is all about making computers understand and generate human language.
The essence of Natural Language Processing lies in making computers understand the natural language. That’s not an easy task though. Computers can understand the structured form of data like spreadsheets and the tables in the database, but human languages, texts, and voices form an unstructured category of data, and it gets difficult for the computer to understand it, and there arises the need for Natural Language Processing.
It’s very difficult for a computer to extract the exact meaning from a sentence. For example – The boy radiated fire like vibes. The boy had a very motivating personality or he actually radiated fire? As you see over here, parsing English with a computer is going to be complicated.
There are various stages involved in training a model. Solving a complex problem in Machine Learning means building a pipeline. In simple terms, it means breaking a complex problem into a number of small problems, making models for each of them and then integrating these models. A similar thing is done in NLP. We can break down the process of understanding English for a model into a number of small pieces.