Natural Language Processing
Abstract
In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or categorical. Generally, not much processing is required in those datasets because computers are great with standardized and structured data. While trying to apply those machine learning techniques to human language though, it opens up a whole new box of challenges as language is not precise and holds different meanings in different contexts. Even the basic structure changes when we move from one language to another. Hence language needs special consideration during the creation of intelligent applications, and this is grouped under the domain of Natural Language Processing (NLP).