Introduction to NLP and the Course

Related lectures (32)

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

Delves into advancements and challenges in NLP, along with ethical considerations and potential harms.

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

Covers functions, including even and odd functions, periodicity, and function operations.

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Introduces the CYK parsing algorithm for context-free grammars, explaining its handling of ambiguity and importance in parsing general grammars.

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

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Covers multithreading, code generation, and type checking in compiler implementation.

Explores lexical semantics, word sense, semantic relations, and WordNet, highlighting applications in language engineering and information retrieval.

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

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

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