Lectures related to Keyphrase Extraction

Introduction to Modern Natural Language Processing

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

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

NLP Pre-processing: Tokenization, Stop Words, Lemmatization

Covers tokenization, stop words removal, and lemmatization for NLP tasks.

Text Understanding

Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.

Word Embeddings: Introduction and Applications

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Coreference Resolution

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Neural Word Embeddings: Learning Representations for Natural Language

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

Deep Learning for Question Answering

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

Handling Text Data: Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.

Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Music Streaming Research: Natural Language Processing at Deezer

Showcases a student's internship at Deezer's research team working on natural language processing.

Modern NLP: Data Collection, Annotation & Biases

Explores data annotation in NLP and the impact of biases on model fine-tuning.

Internship Report: Hangqian Li

Covers an internship report at Tencent Al lab, focusing on 3D avatar reconstruction and neural rendering techniques.

Ethical Considerations in Natural Language Processing

Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.

Natural Language Processing: Understanding Transformers and Tokenization Log in to Mediaspace to watch this video

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

Neural Networks for NLP Log in to Mediaspace to watch this video

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Question Answering: Deep Learning Insights Log in to Mediaspace to watch this video

Explores question answering systems, reading comprehension models, and the challenges in achieving accurate responses.

Foundations of Information Systems: Course Overview and Key Concepts Log in to Mediaspace to watch this video

Introduces the course on information systems, covering its structure, objectives, and foundational concepts essential for understanding data management and decision-making.