Coreference Resolution

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Related lectures (32)

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Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

The Future: Computational Linguistics and Deep Learning

Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.

Compositional Representations and Systematic Generalization

Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.

Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Multilingual NLP: Challenges and Innovations

Covers the importance of multilingual NLP and the challenges in scaling language models.

Introduction to Natural Language Processing

Introduces the basics of Natural Language Processing, covering challenges, application domains, and linguistic processing levels.

Sparse Communication: Transformations and Applications

Explores the evolution from sparse modeling to sparse communication in neural networks for natural language processing tasks.

Transformer Architecture: Subquadratic Attention Mechanisms

Covers transformer architecture, focusing on encoder-decoder models and subquadratic attention mechanisms for efficient processing of input sequences.

Deep Learning Techniques: Recurring Networks and LSTM Models

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

Neural Word Embeddings: Learning Representations for Natural Language

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