History of NLP

NLP is a field that has emerged from various other fields such as artificial intelligence, linguistics, and data science. With the advancement of computing technologies and the increased availability of data, NLP has undergone a huge change. Previously, a traditional rule-based system was used for computations, in which you had to explicitly write hardcoded rules. Today, computations on natural language are being done using machine learning and deep learning techniques.

Consider an example. Let's say we have to extract the names of some politicians from a set of political news articles. So, if we want to apply rule-based grammar, we must manually craft certain rules based on human understanding of language. Some of the rules for extracting a person's name can be that the word should be a proper noun, every word should start with a capital letter, and so on. As we can see, using a rule-based system like this would not yield very accurate results.

Rule-based systems do work well in some cases, but the disadvantages far outweigh the advantages. One major disadvantage is that the same rule cannot be applicable in all cases, given the complex and nuanced nature of most language. These disadvantages can be overcome by using machine learning, where we write an algorithm that tries to learn a language using the text corpus (training data) rather than us explicitly programming it to do so.