Natural Language Handling

Natural Language Handling

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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

Regexp Tokenizer

Data Visualization

Contenido del Curso

Natural Language Handling

Natural Language Handling

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

Data Visualization

Now that we've covered the key features of the nltk package, let's move on to visualizing our data. We'll begin by calculating word frequencies and then displaying these frequencies using a bar plot.

Tarea

Calculate the frequency distribution of the tokenized words from your text.
Find the top 30 most common words in this distribution.

Mark tasks as Completed

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Now that we've covered the key features of the nltk package, let's move on to visualizing our data. We'll begin by calculating word frequencies and then displaying these frequencies using a bar plot.

Tarea

Calculate the frequency distribution of the tokenized words from your text.
Find the top 30 most common words in this distribution.

Mark tasks as Completed

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 1. Capítulo 10

AVAILABLE TO ULTIMATE ONLY

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