Lecture

NLP Pre-processing: Tokenization, Stop Words, Lemmatization

Graph Chat

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.

In course

EE-608: Deep Learning For Natural Language Processing

This course covers advanced topics in deep learning architectures for natural language processing. The focus is on attention-based architectures, structure processing and variational-Bayesian approaches, and why these models are particularly suited to the ...

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.

Instructor

James Henderson

Teaching & PhD PhD Students Haruki Shirakami, Dina El Zein, Jose Rafael Espinosa Mena, Kimia Alavi, Mojtaba Nafez Past EPFL PhD Students as codirector Lesly Sadiht Miculicich Werlen, Rabeeh Karimi Mahabadi, Alireza Mohammadshahi, Andreas Thomas Marfurt, Jan Frederik Jonas Florian Mai, Melika Behjati, Fabio James Fehr, Andrei Catalin Coman Courses Deep Learning For Natural Language Processing EE-608 This course covers advanced topics in deep learning architectures for natural language proce

Official source

Related concepts (32)

Text mining

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al.

Natural-language understanding

Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

Natural language generation

Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information". While it is widely agreed that the output of any NLG process is text, there is some disagreement about whether the inputs of an NLG system need to be non-linguistic.

PyTorch

PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. It is free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, Uber's Pyro, Hugging Face's Transformers, PyTorch Lightning, and Catalyst.

Natural Language Toolkit

The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data.

Related lectures (64)

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Neural Word Embeddings: Learning Representations for Natural Language

Covers neural word embeddings and methods for learning word representations in natural language processing.

Introduction to Modern Natural Language Processing

Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.

Deep Learning for Question Answering

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

Language Models: Fixed-context and Recurrent Neural Networks