Information Extraction: Methods and Applications

Related lectures (29)

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Explores knowledge inference for graphs, discussing label propagation, optimization objectives, and probabilistic behavior.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores Entity Disambiguation, linking text mentions to a knowledge base, coherence in entity graphs, and Personalized PageRank.

Explores document classification methods, including Naïve Bayes and word embeddings.

Explores Named Entity Recognition, its uses, techniques, and applications in information extraction.

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Introduces students to digital humanities through practical exercises, such as working on a large ongoing project.

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.