Introduction to NLP and the Course

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Related lectures (32)

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Introduction to Natural Language Processing

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

Parsing: CYK Algorithm

Explores formal grammars, parsing algorithms, CYK algorithm efficiency, and syntactic correctness in Natural Language Processing.

Control Structures; Functions

Covers control structures, loops, and functions in C language, emphasizing the importance of local variables and avoiding global variables.

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

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.

Music and Emotion: Understanding Expressive Communication

Explores the communication differences between music and language, models of emotion, and how emotions can be measured in music.

CYK algorithm

Introduces the CYK algorithm for efficient syntactic analysis using chart parsing and discusses its complexity and bottom-up parsing technique.

Multilingual NLP: Challenges and Innovations

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

Wave Equation and Laplacian

Explores the wave equation, Laplacian properties, and non-dependent functions in neighborhoods.

Natural Language Processing: Understanding Transformers and Tokenization

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