Handling Text Data: Document Retrieval and Classification

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Coreference Resolution

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

Natural Language Processing Basics

Covers the basics of Natural Language Processing, including tokenization, part-of-speech tagging, and embeddings, and explores practical applications like sentiment analysis.

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Neural Word Embeddings

Introduces neural word embeddings and dense vector representations for natural language processing.

Ethical Considerations in Natural Language Processing

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

Deep Learning for NLP

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Question Answering: Challenges and Solutions

Explores challenges in question answering systems and solutions, including fine-tuning and open-domain retrieval.

Neural Word Embeddings: Learning Representations for Natural Language

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

Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Information retrieval: vector space

Covers the basics of information retrieval using vector space models and practical exercises on relevance feedback and posting list scanning.