Natural Language Processing
This course provides an understanding of natural language processing, its tools, techniques, philosophy and principle. Topics covered include sound, words and word forms, structures, meaning, and web 2.0 applications
Scientific Text Mining and Knowledge Graphs
Explore scientific text mining and knowledge graphs, focusing on sequence labeling, multi-output models, and ensemble learning for information extraction and table component analysis.
Neural Nets for NLP - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP using local independence assumptions and conditional random fields. Learn about sequence labeling, globally normalized models, and advanced training techniques.
Neural Nets for NLP - Structured Prediction Basics
Explore structured prediction in NLP, covering perceptron algorithms, max-margin objectives, and exposure bias remedies. Learn techniques for modeling output interactions and training globally normalized models.
CMU Neural Nets for NLP - Structured Prediction Basics
Explore structured prediction in NLP, covering sequence labeling, model interactions, training algorithms, and cost-augmented decoding for improved language understanding and generation.
Neural Nets for NLP 2021 - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP, focusing on local independence assumptions and Conditional Random Fields. Learn about sequence labeling, potential functions, and dynamic programming models.
CMU Multilingual NLP 2020 - Advanced Text Classification-Labeling
Explore advanced text classification and sequence labeling methods, including subword models, unsupervised training, and structured prediction for multilingual NLP applications.
CMU Multilingual NLP 2020 - Text Classification and Sequence Labeling
Explore text classification and sequence labeling in multilingual NLP, covering models, datasets, and neural networks for various language processing tasks.
Neural Nets for NLP 2020 - Structured Prediction with Local Independence Assumptions
Explore structured prediction in NLP, covering local independence assumptions and Conditional Random Fields for improved sequence labeling and output consistency.