Identifying the Most Frequent Words in Text

Identifying the Most Frequent Words in Text

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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.

List Comprehension

Lemmatizer

Regexp Tokenizer

Data Visualization

Course Content

Identifying the Most Frequent Words in Text

Identifying the Most Frequent Words in Text

Introduction Import NLTK Tokenization Stopwords List Comprehension Stemming Tagging Lemmatizer Regexp Tokenizer Data Visualization Word Cloud

Lemmatizer

A lemmatizer is a critical tool in natural language processing that reduces words to their base form or lemma. By simplifying words to their base forms, a lemmatizer enhances both the accuracy and efficiency of various natural language processing algorithms and techniques.

Task

Import the WordNetLemmatizer class for lemmatization from NLTK.
Create an instance of the WordNetLemmatizer.
Apply lemmatization to each word in the list of stemmed words.

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A lemmatizer is a critical tool in natural language processing that reduces words to their base form or lemma. By simplifying words to their base forms, a lemmatizer enhances both the accuracy and efficiency of various natural language processing algorithms and techniques.

Task

Import the WordNetLemmatizer class for lemmatization from NLTK.
Create an instance of the WordNetLemmatizer.
Apply lemmatization to each word in the list of stemmed words.

Mark tasks as Completed

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 8

AVAILABLE TO ULTIMATE ONLY

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