Tasks of Natural Language Processing

Overview of the tasks

NLP has a multitude of real-world applications. A good NLP system is one that performs many NLP tasks. When we search for today’s weather on Google or use Google Translate to find out how to say, “How are you?” in French, we rely on a subset of such tasks in NLP. We’ll list some of the most ubiquitous tasks here, and this course covers most of these:

• Tokenization: Tokenization is the task of separating a text corpus into atomic units (for example, words or characters). Although it may seem trivial for a language like English, tokenization is an important task. For example, in the Japanese language, words are not delimited by spaces or punctuation marks.

• Word-sense disambiguation (WSD): WSD is the task of identifying the correct meaning of a word. For example, in the sentences “The dog barked at the mail carrier” and “Tree bark is sometimes used as medicine,” the word “bark” has two different meanings. WSD is critical for tasks such as question answering.