Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.
Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.
By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.
Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.
Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
Explores formal grammars, parsing algorithms, CYK algorithm efficiency, and syntactic correctness in Natural Language Processing.
Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.
