Natural Language Processing Basics

Related lectures (32)

Page 3 of 4

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

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

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Covers the role of Transformers in unifying various machine learning fields.

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

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers the basics of Natural Language Processing, including challenges, linguistic processing levels, and the impact of power laws.

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

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