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

Tagging

Regexp Tokenizer

Data Visualization

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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

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Tagging

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.

Завдання

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Download the part-of-speech tagger from NLTK.
Perform part-of-speech tagging on the list of stemmed words.

Mark tasks as Completed

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