Lecture

NLP Pre-processing: Tokenization, Stop Words, Lemmatization

Description

This lecture covers the pre-processing steps for Natural Language Processing tasks, focusing on tokenization, stop words removal, and lemmatization. The instructor guides through the process of preparing text data for sentiment analysis using Python libraries like NLTK and Spacy. The lecture includes practical examples of tokenizing text, removing stop words, and reducing words to their base form. Students will learn how to implement these techniques in a step-by-step manner and understand their importance in text analysis tasks.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.