Summary  
This chapter explains how to assign part-of-speech labels to words in a text using tagging techniques provided by libraries like NLTK.

General domain of usage  
Natural language processing

Tagging involves **assigning labels or tags** to words in a text, identifying their **part of speech**. NLTK offers a range of taggers, notably the **popular POS tagger**.

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data. Our focus will encompass several core areas of natural language processing: tokenization, stemming, tagging and parsing. These NLTK features will form the backbone of our text processing and analysis tasks, making it an essential tool in our project for handling and extracting meaningful insights from language data.

In this project, we will be utilizing the capabilities of the Natural Language Toolkit (NLTK), a versatile and comprehensive library in Python designed for working with human language data.

Identifying the Most Frequent Words in Text

Tagging

Ratkaisu

Identifying the Most Frequent Words in Text

Tagging

Ratkaisu