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Coreference Resolution
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Related lectures (38)
Deep Learning for Question Answering
Explores deep learning for question answering, analyzing neural networks and model robustness to noise.
Sequence to Sequence Models: Overview and Applications
Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.
Contextual Representations: ELMO and BERT Overview
Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.
Introduction to Modern Natural Language Processing
Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.
Language Models: Fixed-context and Recurrent Neural Networks
Discusses language models, focusing on fixed-context neural models and recurrent neural networks.
Ethical Considerations in Natural Language Processing
Explores ethical challenges in NLP systems, including biases, toxicity, privacy, and disinformation.
Classical Language Models: Foundations and Applications
Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.
Transformers: Revolutionizing Attention Mechanisms in NLP
Covers the development of transformers and their impact on attention mechanisms in NLP.
Text Understanding
Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.
Recurrent Neural Networks: Training and Challenges
Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.
Natural Language Generation
Explores Natural Language Generation, covering neural models, biases, ethics, and evaluation challenges.
Binary Sentiment Classifier Training
Covers the training of a binary sentiment classifier using an RNN.
Handling Text Data: Document Retrieval and Classification
Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.
Sparse Communication: Transformations and Applications
Explores the evolution from sparse modeling to sparse communication in neural networks for natural language processing tasks.
Pretraining Sequence-to-Sequence Models: BART and T5
Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.
Coreference Resolution
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Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.
Natural Language Processing: Understanding Transformers and Tokenization
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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
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Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Model Analysis
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Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Neuro-symbolic Representations: Commonsense Knowledge & Reasoning
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Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.
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