POS tagging and NER tagging [Video]

First up, **POS tagging**. This process labels each word in a sentence with its part of speech—like nouns, verbs, adjectives, and so on. For example, in the sentence ‘The cat sat on the mat,’ ‘cat’ is tagged as a noun and ‘sat’ as a verb.

Why is this important? POS tagging helps machines understand the structure and function of words in a sentence, improving tasks like **text analysis** and **machine translation**.”

Now, onto **NER tagging**. This identifies and classifies entities in a text—like names, dates, locations, and more. For instance, in ‘Barack Obama was born in Hawaii,’ ‘Barack Obama’ is tagged as a person, and ‘Hawaii’ as a location.

NER is crucial for tasks like **information extraction**, **search engines**, and **chatbots**, where understanding specific entities helps in delivering accurate results.

So, to recap: **POS tagging** breaks down sentence structure, while **NER tagging** highlights key entities. Together, they make NLP models smarter and more context-aware.”

These techniques are essential for everything from grammar checks to AI assistants.