Neural Network Syntactic Parsing

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Related lectures (32)

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Syntactic Parsing and Graph2Graph Transformers

Covers syntactic parsing, dependency grammar, and graph-based neural network parsing.

Syntactic Parsing: Dependency Structure

Covers syntactic structure, dependency parsing, and neural network transition-based parsing, highlighting the importance of dependency structure in linguistic analysis.

Sequence to Sequence Models: Overview and Applications

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Recurrent Neural Networks: Training and Challenges

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

CYK Algorithm for Parsing General Grammars

Explores parsing general grammars using the CYK algorithm and Chomsky Normal Form.

Syntax Trees: Parse vs AST

Explains syntax trees, parse vs AST, while language syntax, AST classes, and grammar ambiguities.

Neural Networks for NLP

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

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Long Short-Term Memory Networks

Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.