Natural Language Generation: Evaluating Text Quality

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Neural Word Embeddings: Learning Representations for Natural Language

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

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Introduction to Modern Natural Language Processing

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

Foundations of Information Systems: Course Overview and Key Concepts

Introduces the course on information systems, covering its structure, objectives, and foundational concepts essential for understanding data management and decision-making.

Word Embeddings: Modeling Word Context and Similarity

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

Natural Language Generation

Explores Natural Language Generation, covering neural models, biases, ethics, and evaluation challenges.

Pretraining Sequence-to-Sequence Models: BART and T5

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

Language Models: Fixed-context and Recurrent Neural Networks

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

Natural Language Processing: A Primer

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

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.