2- Course Aim :-

The goal of this course is to give a broad but detailed introduction to the key algorithms and modeling techniques used for Natural Language Processing (NLP) today. With a few exceptions, NLP involves taking a sequence of words as input (e.g. a sentence) and returning some annotation(s) for that string. Well-known examples of this include part-of-speech tagging and syntactic parsing. Many other common tasks, e.g. shallow parsing or named-entity recognition, can be easily recast as tagging tasks; hence certain basic techniques can be widely applied within NLP. Applications such as automatic speech recognition, machine translation, information extraction, and question answering all make use of NLP techniques. By the end of this course, you should understand how to approach common natural language problems arising in these and other applications.